Delete app111.py

app111.py

@@ -1,1618 +0,0 @@
-import streamlit as st
-import pandas as pd
-import os
-import json
-import base64
-import random
-from streamlit_pdf_viewer import pdf_viewer
-from langchain.prompts import PromptTemplate
-from datetime import datetime
-from pathlib import Path
-from openai import OpenAI
-from dotenv import load_dotenv
-import warnings
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-warnings.filterwarnings('ignore')
-
-os.getenv("OAUTH_CLIENT_ID")
-
-
-# # Load environment variables and initialize the OpenAI client to use Hugging Face Inference API.
-# load_dotenv()
-# client = OpenAI(
-#     base_url="https://api-inference.huggingface.co/v1",
-#     #api_key=os.environ.get('TOKEN2') # Hugging Face API token
-#     api_key=os.environ.get('LLM') 
-# )
-#######
-#from openai import OpenAI
-
-client = OpenAI(
-    base_url="https://router.huggingface.co/hf-inference/models/meta-llama/Llama-3.3-70B-Instruct/v1",
-    #api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
-    api_key=os.environ.get('LLM')
-)
-
-completion = client.chat.completions.create(
-    model="meta-llama/Llama-3.3-70B-Instruct",
-    messages=[
-        {
-            "role": "user",
-            "content": "What is the capital of France?"
-        }
-    ],
-)
-
-print(completion.choices[0].message)
-#######
-#####
-# from openai import OpenAI
-
-# client = OpenAI(
-#     base_url="https://router.huggingface.co/together/v1",
-#     #api_key="hf_XXXXX",
-#     api_key=os.environ.get('TOKEN2'), # Hugging Face API token
-# )
-# #meta-llama/Meta-Llama-3-8B-Instruct
-# completion = client.chat.completions.create(
-#     #model="meta-llama/Meta-Llama-3-8B-Instruct-Turbo",
-#     model="meta-llama/Meta-Llama-3-8B-Instruct",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": "What is the capital of France?"
-#         }
-#     ],
-# )
-
-#print(completion.choices[0].message)
-#####
-##########################################################3
-# import streamlit as st
-# from transformers import AutoModelForCausalLM, AutoTokenizer
-# import torch
-
-# # Model selection dropdown
-# selected_model = st.selectbox(
-#     "Select Model",
-#     ["meta-llama/Meta-Llama-3-8B-Instruct-Turbo", 
-#      "meta-llama/Llama-3.3-70B-Instruct", 
-#      "meta-llama/Llama-3.2-3B-Instruct",
-#      "meta-llama/Llama-4-Scout-17B-16E-Instruct", 
-#      "meta-llama/Meta-Llama-3-8B-Instruct",
-#      "meta-llama/Llama-3.1-70B-Instruct"],
-#     key='model_select'
-# )
-
-# @st.cache_resource  # Cache the model to prevent reloading
-# def load_model(model_name):
-#     try:
-#         # Optimized model loading configuration
-#         model = AutoModelForCausalLM.from_pretrained(
-#             model_name,
-#             torch_dtype=torch.float16,        # Use half precision
-#             device_map="auto",                # Automatic device mapping
-#             load_in_8bit=True,               # Enable 8-bit quantization
-#             low_cpu_mem_usage=True,          # Optimize CPU memory usage
-#             max_memory={0: "10GB"}           # Limit GPU memory usage
-#         )
-        
-#         tokenizer = AutoTokenizer.from_pretrained(
-#             model_name,
-#             padding_side="left",
-#             truncation_side="left"
-#         )
-        
-#         return model, tokenizer
-    
-#     except Exception as e:
-#         st.error(f"Error loading model: {str(e)}")
-#         return None, None
-
-# # Load the selected model with optimizations
-# if selected_model:
-#     model, tokenizer = load_model(selected_model)
-
-#     # Check if model loaded successfully
-#     if model is not None:
-#         st.success(f"Successfully loaded {selected_model}")
-#     else:
-#         st.warning("Please select a different model or check your hardware capabilities")
-
-# # Function to generate text
-# def generate_response(prompt, model, tokenizer):
-#     try:
-#         inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
-        
-#         with torch.no_grad():
-#             outputs = model.generate(
-#                 inputs["input_ids"],
-#                 max_length=256,
-#                 num_return_sequences=1,
-#                 temperature=0.7,
-#                 do_sample=True,
-#                 pad_token_id=tokenizer.pad_token_id
-#             )
-        
-#         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#         return response
-    
-#     except Exception as e:
-#         return f"Error generating response: {str(e)}"
-############################################################
-
-####new
-# from openai import OpenAI
-
-# client = OpenAI(
-#     base_url="https://router.huggingface.co/together/v1",
-#     api_key=os.environ.get('TOKEN2'),
-# )
-
-# completion = client.chat.completions.create(
-#     model="meta-llama/Meta-Llama-3-8B-Instruct-Turbo",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": "What is the capital of France?"
-#         }
-#     ],
-#     max_tokens=512,
-# )
-
-# print(completion.choices[0].message)
-#####
-
-# Create necessary directories
-for dir_name in ['data', 'feedback']:
-    if not os.path.exists(dir_name):
-        os.makedirs(dir_name)
-
-# Custom CSS
-st.markdown("""
-<style>
-    .stButton > button {
-        width: 100%;
-        margin-bottom: 10px;
-        background-color: #4CAF50;
-        color: white;
-        border: none;
-        padding: 10px;
-        border-radius: 5px;
-    }
-    .task-button {
-        background-color: #2196F3 !important;
-    }
-    .stSelectbox {
-        margin-bottom: 20px;
-    }
-    .output-container {
-        padding: 20px;
-        border-radius: 5px;
-        border: 1px solid #ddd;
-        margin: 10px 0;
-    }
-    .status-container {
-        padding: 10px;
-        border-radius: 5px;
-        margin: 10px 0;
-    }
-    .sidebar-info {
-        padding: 10px;
-        background-color: #f0f2f6;
-        border-radius: 5px;
-        margin: 10px 0;
-    }
-    .feedback-button {
-        background-color: #ff9800 !important;
-    }
-    .feedback-container {
-        padding: 15px;
-        background-color: #f5f5f5;
-        border-radius: 5px;
-        margin: 15px 0;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Helper functions
-def read_csv_with_encoding(file):
-    encodings = ['utf-8', 'latin1', 'iso-8859-1', 'cp1252']
-    for encoding in encodings:
-        try:
-            return pd.read_csv(file, encoding=encoding)
-        except UnicodeDecodeError:
-            continue
-    raise UnicodeDecodeError("Failed to read file with any supported encoding")
-
-#def save_feedback(feedback_data):
-    #feedback_file = 'feedback/user_feedback.csv'
-    #feedback_df = pd.DataFrame([feedback_data])
-    
-    #if os.path.exists(feedback_file):
-        #feedback_df.to_csv(feedback_file, mode='a', header=False, index=False)
-    #else:
-        #feedback_df.to_csv(feedback_file, index=False)
-
-def reset_conversation():
-    st.session_state.conversation = []
-    st.session_state.messages = []
-    if 'task_choice' in st.session_state:
-        del st.session_state.task_choice
-    return None
-    #new 24 March
-    #user_input = st.text_input("Enter your prompt:")
-###########33
-
-# Initialize session state variables
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-if "examples_to_classify" not in st.session_state:
-    st.session_state.examples_to_classify = []
-if "system_role" not in st.session_state:
-    st.session_state.system_role = ""
-
-    
-
-# Main app title
-st.title("🤖🦙 Text Data Labeling and Generation App")
-# def embed_pdf_sidebar(pdf_path):
-#     with open(pdf_path, "rb") as f:
-#         base64_pdf = base64.b64encode(f.read()).decode('utf-8')
-#     pdf_display = f"""
-#         <iframe src="data:application/pdf;base64,{base64_pdf}" 
-#         width="100%" height="400" type="application/pdf"></iframe>
-#     """
-#     st.markdown(pdf_display, unsafe_allow_html=True)
-# 
-        
-    
-# Sidebar settings
-with st.sidebar:
-    st.title("⚙️ Settings")
-   
-
-#this last code works
-with st.sidebar:
-    st.markdown("### 📘Data Generation and Labeling Instructions")
-    #st.markdown("<h4 style='color: #4A90E2;'>📘    Instructions</h4>", unsafe_allow_html=True)
-    with open("User instructions.pdf", "rb") as f:
-        st.download_button(
-            label="📄 Download Instructions PDF",
-            data=f,
-            #file_name="instructions.pdf",
-            file_name="User instructions.pdf",
-            mime="application/pdf"
-        )
-   
-    selected_model = st.selectbox(
-        "Select Model",
-        ["meta-llama/Llama-Prompt-Guard-2-86M","mistralai/Mistral-7B-Instruct-v0.2", "meta-llama/Llama-3.2-11B-Vision-Instruct","meta-llama/Meta-Llama-3-8B-Instruct-Turbo", "meta-llama/Llama-3.3-70B-Instruct", "meta-llama/Llama-3.2-3B-Instruct","meta-llama/Llama-4-Scout-17B-16E-Instruct", "meta-llama/Meta-Llama-3-8B-Instruct",
-         "meta-llama/Llama-3.1-70B-Instruct"],
-        key='model_select'
-    )
-
-#################new oooo
-
-# # Model selection dropdown
-# selected_model = st.selectbox(
-#     "Select Model",
-#     [#"meta-llama/Meta-Llama-3-8B-Instruct-Turbo",
-#      "meta-llama/Llama-3.2-3B-Instruct",
-#      "meta-llama/Llama-3.3-70B-Instruct", 
-#      "meta-llama/Llama-3.2-3B-Instruct",
-#      "meta-llama/Llama-4-Scout-17B-16E-Instruct", 
-#      "meta-llama/Meta-Llama-3-8B-Instruct",
-#      "meta-llama/Llama-3.1-70B-Instruct"],
-#     key='model_select'
-# )
-
-# @st.cache_resource  # Cache the model to prevent reloading
-# def load_model(model_name):
-#     try:
-#         # Optimized model loading configuration
-#         model = AutoModelForCausalLM.from_pretrained(
-#             model_name,
-#             torch_dtype=torch.float16,        # Use half precision
-#             device_map="auto",                # Automatic device mapping
-#             load_in_8bit=True,               # Enable 8-bit quantization
-#             low_cpu_mem_usage=True,          # Optimize CPU memory usage
-#             max_memory={0: "10GB"}           # Limit GPU memory usage
-#         )
-        
-#         tokenizer = AutoTokenizer.from_pretrained(
-#             model_name,
-#             padding_side="left",
-#             truncation_side="left"
-#         )
-        
-#         return model, tokenizer
-    
-#     except Exception as e:
-#         st.error(f"Error loading model: {str(e)}")
-#         return None, None
-
-# # Load the selected model with optimizations
-# if selected_model:
-#     model, tokenizer = load_model(selected_model)
-
-#     # Check if model loaded successfully
-#     if model is not None:
-#         st.success(f"Successfully loaded {selected_model}")
-#     else:
-#         st.warning("Please select a different model or check your hardware capabilities")
-
-# # Function to generate text
-# def generate_response(prompt, model, tokenizer):
-#     try:
-#         inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
-        
-#         with torch.no_grad():
-#             outputs = model.generate(
-#                 inputs["input_ids"],
-#                 max_length=256,
-#                 num_return_sequences=1,
-#                 temperature=0.7,
-#                 do_sample=True,
-#                 pad_token_id=tokenizer.pad_token_id
-#             )
-        
-#         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#         return response
-    
-#     except Exception as e:
-#         return f"Error generating response: {str(e)}"
-# ################
-
-# model = AutoModelForCausalLM.from_pretrained(
-#     "meta-llama/Meta-Llama-3-8B-Instruct",
-#     torch_dtype=torch.float16,  # Use half precision
-#     device_map="auto",          # Automatic device mapping
-#     load_in_8bit=True          # Load in 8-bit precision
-# )
-    temperature = st.slider(
-        "Temperature",
-        0.0, 1.0, 0.7,
-        help="Controls randomness in generation"
-    )
-    
-    st.button("🔄 New Conversation", on_click=reset_conversation)  
-    with st.container():
-        st.markdown(f"""
-           <div class="sidebar-info">
-               <h4>Current Model: {selected_model}</h4>
-               <p><em>Note: Generated content may be inaccurate or false. Check important info.</em></p>
-           </div>
-            """, unsafe_allow_html=True)
-    
-    feedback_url = "https://docs.google.com/forms/d/e/1FAIpQLSdZ_5mwW-pjqXHgxR0xriyVeRhqdQKgb5c-foXlYAV55Rilsg/viewform?usp=header"
-    st.sidebar.markdown(
-        f'<a href="{feedback_url}" target="_blank"><button style="width: 100%;">Feedback Form</button></a>',
-        unsafe_allow_html=True
-    )
-
-# Display conversation
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Main content
-if 'task_choice' not in st.session_state:
-    col1, col2 = st.columns(2)
-    with col1:
-        if st.button("📝 Data Generation", key="gen_button", help="Generate new data"):
-            st.session_state.task_choice = "Data Generation"
-    with col2:
-        if st.button("🏷️ Data Labeling", key="label_button", help="Label existing data"):
-            st.session_state.task_choice = "Data Labeling"
-
-if "task_choice" in st.session_state:
-    if st.session_state.task_choice == "Data Generation":
-        st.header("📝 Data Generation")
-
-       # 1. Domain selection
-        domain_selection = st.selectbox("Domain", [
-            "Restaurant reviews", "E-Commerce reviews", "News", "AG News", "Tourism", "Custom"
-        ])
-        
-        # 2. Handle custom domain input
-        custom_domain_valid = True  # Assume valid until proven otherwise
-        
-        if domain_selection == "Custom":
-            domain = st.text_input("Specify custom domain")
-            if not domain.strip():
-                st.error("Please specify a domain name.")
-                custom_domain_valid = False
-        else:
-            domain = domain_selection
-
-        # Classification type selection
-        classification_type = st.selectbox(
-            "Classification Type",
-            ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
-        )
-        # Labels setup based on classification type
-        #labels = []
-        labels = []
-        labels_valid = False
-        errors = []
-
-        def validate_binary_labels(labels):
-            errors = []
-            normalized = [label.strip().lower() for label in labels]
-        
-            if not labels[0].strip():
-                errors.append("First class name is required.")
-            if not labels[1].strip():
-                errors.append("Second class name is required.")
-            if normalized[0] == normalized[1] and all(normalized):
-                errors.append("Class names must be different.")
-            return errors
-        
-        if classification_type == "Sentiment Analysis":
-            st.write("### Sentiment Analysis Labels (Fixed)")
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                st.text_input("First class", "Positive", disabled=True)
-            with col2:
-                st.text_input("Second class", "Negative", disabled=True)
-            with col3:
-                st.text_input("Third class", "Neutral", disabled=True)
-            labels = ["Positive", "Negative", "Neutral"]
-        
-        elif classification_type == "Binary Classification":
-            st.write("### Binary Classification Labels")
-            col1, col2 = st.columns(2)
-            with col1:
-                label_1 = st.text_input("First class", "Positive")
-            with col2:
-                label_2 = st.text_input("Second class", "Negative")
-            
-            labels = [label_1, label_2]
-            errors = validate_binary_labels(labels)
-        
-            if errors:
-                st.error("\n".join(errors))
-            else:
-                st.success("Binary class names are valid and unique!")
-
-        
-        elif classification_type == "Multi-Class Classification":
-            st.write("### Multi-Class Classification Labels")
-        
-            default_labels_by_domain = {
-                "News": ["Political", "Sports", "Entertainment", "Technology", "Business"],
-                "AG News": ["World", "Sports", "Business", "Sci/Tech"],
-                "Tourism": ["Accommodation", "Transportation", "Tourist Attractions", 
-                            "Food & Dining", "Local Experience", "Adventure Activities",
-                            "Wellness & Spa", "Eco-Friendly Practices", "Family-Friendly",
-                            "Luxury Tourism"],
-                "Restaurant reviews": ["Italian", "French", "American"],
-                "E-Commerce reviews": ["Mobile Phones & Accessories", "Laptops & Computers","Kitchen & Dining",
-                                       "Beauty & Personal Care", "Home & Furniture", "Clothing & Fashion",
-                                      "Shoes & Handbags", "Health & Wellness", "Electronics & Gadgets",
-                                       "Books & Stationery","Toys & Games", "Sports & Fitness",
-                                       "Grocery & Gourmet Food","Watches & Accessories", "Baby Products"]
-            }
-        
-            num_classes = st.slider("Number of classes", 3, 15, 3)
-        
-            # Get defaults for selected domain, or empty list
-            defaults = default_labels_by_domain.get(domain, [])
-        
-            labels = []
-            errors = []
-            cols = st.columns(3)
-        
-            for i in range(num_classes):
-                with cols[i % 3]:
-                    default_value = defaults[i] if i < len(defaults) else ""
-                    label_input = st.text_input(f"Class {i+1}", default_value)
-                    normalized_label = label_input.strip().title()
-        
-                    if not normalized_label:
-                        errors.append(f"Class {i+1} name is required.")
-                    else:
-                        labels.append(normalized_label)
-        
-            # Check for duplicates (case-insensitive)
-            if len(labels) != len(set(labels)):
-                errors.append("Labels names must be unique (case-insensitive, normalized to Title Case).")
-
-            # Show validation results
-            if errors:
-                for error in errors:
-                    st.error(error)
-            else:
-                st.success("All Labels names are valid and unique!")
-            labels_valid = not errors  # Will be True only if there are no label errors     
-
-                    ##############
-    #new 22/4/2025
-    # add additional attributes
-        add_attributes = st.checkbox("Add additional attributes (optional)")
-        additional_attributes = []
-        
-        if add_attributes:
-            num_attributes = st.slider("Number of attributes to add", 1, 5, 1)
-            for i in range(num_attributes):
-                st.markdown(f"#### Attribute {i+1}")
-                attr_name = st.text_input(f"Name of attribute {i+1}", key=f"attr_name_{i}")
-                attr_topics = st.text_input(f"Topics (comma-separated) for {attr_name}", key=f"attr_topics_{i}")
-                if attr_name and attr_topics:
-                    topics_list = [topic.strip() for topic in attr_topics.split(",") if topic.strip()]
-                    additional_attributes.append({"attribute": attr_name, "topics": topics_list})
-
-################
-        
-        # Generation parameters
-        col1, col2 = st.columns(2)
-        with col1:
-            min_words = st.number_input("Min words", 1, 100, 20)
-        with col2:
-            max_words = st.number_input("Max words", min_words, 100, 50)
-
-        # Few-shot examples
-        use_few_shot = st.toggle("Use few-shot examples")
-        few_shot_examples = []
-        if use_few_shot:
-            num_examples = st.slider("Number of few-shot examples", 1, 10, 1)
-            for i in range(num_examples):
-                with st.expander(f"Example {i+1}"):
-                    content = st.text_area(f"Content", key=f"few_shot_content_{i}")
-                    label = st.selectbox(f"Label", labels, key=f"few_shot_label_{i}")
-                    if content and label:
-                        few_shot_examples.append({"content": content, "label": label})
-
-        num_to_generate = st.number_input("Number of examples", 1, 100, 10)
-        #sytem role after
-         # System role customization
-        #default_system_role = f"You are a professional {classification_type} expert, your role is to generate text examples for {domain} domain. Always generate unique diverse examples and do not repeat the generated data. The generated text should be between {min_words} to {max_words} words long."
-        # System role customization
-        default_system_role = (
-    f"You are a seasoned expert in {classification_type}, specializing in the {domain} domain. "
-    f" Your primary responsibility is to generate high-quality, diverse, and unique text examples "
-    f"tailored to this domain. Please ensure that each example adheres to the specified length "
-    f"requirements, ranging from {min_words} to {max_words} words, and avoid any repetition in the generated content."
-)
-        system_role = st.text_area("Modify System Role (optional)", 
-                                value=default_system_role,
-                                key="system_role_input")
-        st.session_state['system_role'] = system_role if system_role else default_system_role
-        # Labels initialization
-        #labels = []
-        
-        
-        user_prompt = st.text_area("User Prompt (optional)")
-
-        # Updated prompt template including system role
-        prompt_template = PromptTemplate(
-            input_variables=["system_role", "classification_type", "domain", "num_examples", 
-                           "min_words", "max_words", "labels", "user_prompt", "few_shot_examples", "additional_attributes"],
-            template=(
-               "{system_role}\n"
-                 "- Use the following parameters:\n"
-                "- Generate {num_examples} examples\n"
-                "- Each example should be between {min_words} to {max_words} words long\n"
-                "- Use these labels: {labels}.\n"
-                "- Use the following additional attributes:\n"
-                "- {additional_attributes}\n"
-                "- Generate the examples in this format: 'Example text. Label: label'\n"
-                "- Do not include word counts or any additional information\n"
-                "- Always use your creativity and intelligence to generate unique and diverse text data\n"
-                "- In sentiment analysis, ensure that the sentiment classification is clearly identified as Positive, Negative, or Neutral. Do not leave the sentiment ambiguous.\n"
-                "- In binary sentiment analysis, classify text strictly as either Positive or Negative. Do not include or imply Neutral as an option.\n"
-                "- Write unique examples every time.\n"
-                "- DO NOT REPEAT your gnerated text. \n"
-                "- For each Output, describe it once and move to the next.\n"
-                "- List each Output only once, and avoid repeating details.\n"
-                "- Additional instructions: {user_prompt}\n\n"
-                "- Use the following examples as a reference in the generation process\n\n {few_shot_examples}. \n"
-                "- Think step by step, generate numbered examples, and check each newly generated example to ensure it has not been generated before. If it has, modify it"
-                
-             )
-         )
-        #    template=(
-        #         "{system_role}\n"
-        #         "- Use the following parameters:\n"
-        #         "- Generate {num_examples} examples\n"
-        #         "- Each example should be between {min_words} to {max_words} words long\n"
-        #         "- Use these labels: {labels}.\n"
-        #         "- Use the following additional attributes:\n"
-        #         "{additional_attributes}\n"
-        #         #"- Format each example like this: 'Example text. Label: [label]. Attribute1: [topic1]. Attribute2: [topic2]'\n"
-        #         "- Generate the examples in this format: 'Example text. Label: label'\n"
-        #         "- Additional instructions: {user_prompt}\n"
-        #         "- Use these few-shot examples if provided:\n{few_shot_examples}\n"
-        #         "- Think step by step and ensure examples are unique and not repeated."
-        #     )
-        # )
-        ##########new 22/4/2025
-        formatted_attributes = "\n".join([
-            f"- {attr['attribute']}: {', '.join(attr['topics'])}" for attr in additional_attributes
-        ])
-#######################
-        
-        # Generate system prompt
-        system_prompt = prompt_template.format(
-            system_role=st.session_state['system_role'],
-            classification_type=classification_type,
-            domain=domain,
-            num_examples=num_to_generate,
-            min_words=min_words,
-            max_words=max_words,
-            labels=", ".join(labels),
-            user_prompt=user_prompt,
-            few_shot_examples="\n".join([f"{ex['content']}\nLabel: {ex['label']}" for ex in few_shot_examples]) if few_shot_examples else "",
-            additional_attributes=formatted_attributes
-        )
-        
-
-        # Store system prompt in session state
-        st.session_state['system_prompt'] = system_prompt
-
-        # Display system prompt
-        st.write("System Prompt:")
-        st.text_area("Current System Prompt", value=st.session_state['system_prompt'], 
-                    height=400, disabled=True)
-        
-
-        if st.button("🎯 Generate Examples"):
-            #
-            errors = []
-            if domain_selection == "Custom" and not domain.strip():
-                st.warning("Custom domain name is required.")
-            elif len(labels) != len(set(labels)):
-                st.warning("Class names must be unique.")
-            elif any(not lbl.strip() for lbl in labels):
-                st.warning("All class labels must be filled in.")
-            #else:
-                #st.success("Generating examples for domain: {domain}")
-                    
-            #if not custom_domain_valid:
-                #st.warning("Custom domain name is required.")
-            #elif not labels_valid:
-                #st.warning("Please fix the label errors before generating examples.")
-            #else:
-                # Proceed to generate examples
-                #st.success(f"Generating examples for domain: {domain}")
-                
-            with st.spinner("Generating examples..."):
-                try:
-                    stream = client.chat.completions.create(
-                        model=selected_model,
-                        messages=[{"role": "system", "content": st.session_state['system_prompt']}],
-                        temperature=temperature,
-                        stream=True,
-                        #max_tokens=80000,
-                        max_tokens=4000,
-                        top_p=0.9,
-                       # repetition_penalty=1.2,
-                        #frequency_penalty=0.5,      # Discourages frequent words
-                        #presence_penalty=0.6,  
-                    )
- #st.session_state['system_prompt'] = system_prompt
-                    #new 24 march
-                    st.session_state.messages.append({"role": "user", "content": system_prompt})
-                 # # ####################
-                    response = st.write_stream(stream)
-                    st.session_state.messages.append({"role": "assistant", "content": response})
-                        # Initialize session state variables if they don't exist
-                    if 'system_prompt' not in st.session_state:
-                        st.session_state.system_prompt = system_prompt
-                    
-                    if 'response' not in st.session_state:
-                        st.session_state.response = response
-                    
-                    if 'generated_examples' not in st.session_state:
-                        st.session_state.generated_examples = []
-                    
-                    if 'generated_examples_csv' not in st.session_state:
-                        st.session_state.generated_examples_csv = None
-                    
-                    if 'generated_examples_json' not in st.session_state:
-                        st.session_state.generated_examples_json = None
-                    
-                    # Parse response and generate examples list
-                    examples_list = []
-                    for line in response.split('\n'):
-                        if line.strip():
-                            parts = line.rsplit('Label:', 1)
-                            if len(parts) == 2:
-                                text = parts[0].strip()
-                                label = parts[1].strip()
-                                if text and label:
-                                    examples_list.append({
-                                        'text': text,
-                                        'label': label,
-                                        'system_prompt': st.session_state.system_prompt,
-                                        'system_role': st.session_state.system_role,
-                                        'task_type': 'Data Generation',
-                                        'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                                    })
-
-                                    # example_dict = {
-                                    #     'text': text,
-                                    #     'label': label,
-                                    #     'system_prompt': st.session_state.system_prompt,
-                                    #     'system_role': st.session_state.system_role,
-                                    #     'task_type': 'Data Generation',
-                                    #     'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                                    # }
-                                    # for attr in additional_attributes:
-                                    #     example_dict[attr['attribute']] = random.choice(attr['topics'])
-                                    
-                                    # examples_list.append(example_dict)
-                                   
-
-                    if examples_list:
-                        # Update session state with new data
-                        st.session_state.generated_examples = examples_list
-                        
-                        # Generate CSV and JSON data
-                        df = pd.DataFrame(examples_list)
-                        st.session_state.generated_examples_csv = df.to_csv(index=False).encode('utf-8')
-                        st.session_state.generated_examples_json = json.dumps(examples_list, indent=2).encode('utf-8')
-
-                       # Vertical layout with centered "or" between buttons
-                        st.download_button(
-                            "📥 Download Generated Examples (CSV)",
-                            st.session_state.generated_examples_csv,
-                            "generated_examples.csv",
-                            "text/csv",
-                            key='download-csv-persistent'
-                        )
-                        
-                        # Add space and center the "or"
-                        st.markdown("""
-                        <div style='text-align: left; margin:15px 0; font-weight: 600; color: #666;'>. . . . . .         or</div>
-                        """, unsafe_allow_html=True)
-                        
-                        st.download_button(
-                            "📥 Download Generated Examples (JSON)",
-                            st.session_state.generated_examples_json,
-                            "generated_examples.json",
-                            "application/json",
-                            key='download-json-persistent'
-                        )
-                        # # Display the labeled examples
-                        # st.markdown("##### 📋 Labeled Examples Preview")
-                        # st.dataframe(df, use_container_width=True)
-                        
-                    if st.button("Continue"):
-                        if follow_up == "Generate more examples":
-                            st.experimental_rerun()
-                        elif follow_up == "Data Labeling":
-                            st.session_state.task_choice = "Data Labeling"
-                            st.experimental_rerun()
-
-                except Exception as e:
-                    st.error("An error occurred during generation.")
-                    st.error(f"Details: {e}")
-
-    
-# Lableing Process
-    elif st.session_state.task_choice == "Data Labeling":
-        st.header("🏷️ Data Labeling")
-        
-        domain_selection = st.selectbox("Domain", ["Restaurant reviews", "E-Commerce reviews", "News", "AG News", "Tourism", "Custom"])
-            # 2. Handle custom domain input
-        custom_domain_valid = True  # Assume valid until proven otherwise
-        
-        if domain_selection == "Custom":
-            domain = st.text_input("Specify custom domain")
-            if not domain.strip():
-                st.error("Please specify a domain name.")
-                custom_domain_valid = False
-        else:
-            domain = domain_selection
-
-
-        # Classification type selection
-        classification_type = st.selectbox(
-            "Classification Type",
-            ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification", "Named Entity Recognition (NER)"]
-        )
-#NNew edit
-        # Labels setup based on classification type
-        labels = []
-        labels_valid = False
-        errors = []
-
-        if classification_type == "Sentiment Analysis":
-            st.write("### Sentiment Analysis Labels (Fixed)")
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                label_1 = st.text_input("First class", "Positive", disabled=True)
-            with col2:
-                label_2 = st.text_input("Second class", "Negative", disabled=True)
-            with col3:
-                label_3 = st.text_input("Third class", "Neutral", disabled=True)
-            labels = ["Positive", "Negative", "Neutral"]
-
-
-        elif classification_type == "Binary Classification":
-            st.write("### Binary Classification Labels")
-            col1, col2 = st.columns(2)
-        
-            with col1:
-                label_1 = st.text_input("First class", "Positive")
-            with col2:
-                label_2 = st.text_input("Second class", "Negative")
-        
-            errors = []
-            labels = [label_1.strip(), label_2.strip()]
-            
-
-            # Strip and lower-case labels for validation
-            label_1 = labels[0].strip()
-            label_2 = labels[1].strip()
-            
-            # Check for empty class names
-            if not label_1:
-                errors.append("First class name is required.")
-            if not label_2:
-                errors.append("Second class name is required.")
-            
-            # Check for duplicates (case insensitive)
-            if label_1.lower() == label_2.lower() and label_1 and label_2:
-                errors.append("Class names must be different.")
-            
-            # Show errors or success
-            if errors:
-                for error in errors:
-                    st.error(error)
-            else:
-                st.success("Binary class names are valid and unique!")
-
-                               
-        elif classification_type == "Multi-Class Classification":
-                st.write("### Multi-Class Classification Labels")
-            
-                default_labels_by_domain = {
-                    "News": ["Political", "Sports", "Entertainment", "Technology", "Business"],
-                    "AG News": ["World", "Sports", "Business", "Sci/Tech"],
-                    "Tourism": ["Accommodation", "Transportation", "Tourist Attractions", 
-                                "Food & Dining", "Local Experience", "Adventure Activities",
-                                "Wellness & Spa", "Eco-Friendly Practices", "Family-Friendly",
-                                "Luxury Tourism"],
-                    "Restaurant reviews": ["Italian", "French", "American"],
-                    "E-Commerce reviews": ["Mobile Phones & Accessories", "Laptops & Computers","Kitchen & Dining",
-                                       "Beauty & Personal Care", "Home & Furniture", "Clothing & Fashion",
-                                      "Shoes & Handbags", "Health & Wellness", "Electronics & Gadgets",
-                                       "Books & Stationery","Toys & Games", "Sports & Fitness",
-                                       "Grocery & Gourmet Food","Watches & Accessories", "Baby Products"]
-            }
-        
-              
-            
-                # Ask user how many classes they want to define
-                num_classes = st.slider("Select the number of classes (labels)", min_value=3, max_value=10, value=3)
-            
-                # Use default labels based on selected domain, if available
-                defaults = default_labels_by_domain.get(domain, [])
-            
-                labels = []
-                errors = []
-                cols = st.columns(3)  # For nicely arranged label inputs
-            
-                for i in range(num_classes):
-                    with cols[i % 3]:  # Distribute inputs across columns
-                        default_value = defaults[i] if i < len(defaults) else ""
-                        label_input = st.text_input(f"Label {i + 1}", default_value)
-                        normalized_label = label_input.strip().title()
-            
-                        if not normalized_label:
-                            errors.append(f"Label {i + 1} is required.")
-                        else:
-                            labels.append(normalized_label)
-            
-                # Check for duplicates (case-insensitive)
-                normalized_set = {label.lower() for label in labels}
-                if len(labels) != len(normalized_set):
-                    errors.append("Label names must be unique (case-insensitive).")
-            
-                # Show validation results
-                if errors:
-                    for error in errors:
-                        st.error(error)
-                else:
-                    st.success("All label names are valid and unique!")
-            
-                labels_valid = not errors  # True if no validation errors
-
-        elif classification_type == "Named Entity Recognition (NER)":
-            # # NER entity options
-            # ner_entities = [
-            #     "PERSON - Names of people, fictional characters, historical figures",
-            #     "ORG - Companies, institutions, agencies, teams",
-            #     "LOC - Physical locations (mountains, oceans, etc.)",
-            #     "GPE - Countries, cities, states, political regions",
-            #     "DATE - Calendar dates, years, centuries",
-            #     "TIME - Times, durations",
-            #     "MONEY - Monetary values with currency"
-            # ]
-            # selected_entities = st.multiselect(
-            #      "Select entities to recognize", 
-            #     ner_entities,
-            #     default=["PERSON - Names of people, fictional characters, historical figures",
-            #              "ORG - Companies, institutions, agencies, teams",
-            #             "LOC - Physical locations (mountains, oceans, etc.)",
-            #             "GPE - Countries, cities, states, political regions",
-            #             "DATE - Calendar dates, years, centuries",
-            #             "TIME - Times, durations",
-            #             "MONEY - Monetary values with currency"],
-            #             key="ner_entity_selection"
-            # )
-            #new 22/4/2025
-            #if classification_type == "Named Entity Recognition (NER)":
-            use_few_shot = True
-            #new 22/4/2025
-            few_shot_examples = [
-                {"content": "Mount Everest is the tallest mountain in the world.", "label": "LOC: Mount Everest"},
-                {"content": "The President of the United States visited Paris last summer.", "label": "GPE: United States, GPE: Paris"},
-                {"content": "Amazon is expanding its offices in Berlin.", "label": "ORG: Amazon, GPE: Berlin"},
-                {"content": "J.K. Rowling wrote the Harry Potter books.", "label": "PERSON: J.K. Rowling"},
-                {"content": "Apple was founded in California in 1976.", "label": "ORG: Apple, GPE: California, DATE: 1976"},
-                {"content": "The Nile is the longest river in Africa.", "label": "LOC: Nile, GPE: Africa"},
-                {"content": "He arrived at 3 PM for the meeting.", "label": "TIME: 3 PM"},
-                {"content": "She bought the dress for $200.", "label": "MONEY: $200"},
-                {"content": "The event is scheduled for July 4th.", "label": "DATE: July 4th"},
-                {"content": "The World Health Organization is headquartered in Geneva.", "label": "ORG: World Health Organization, GPE: Geneva"}
-            ]
-###########
-        
-            st.write("### Named Entity Recognition (NER) Entities")
-
-            # Predefined standard entities
-            ner_entities = [
-                "PERSON - Names of people, fictional characters, historical figures",
-                "ORG - Companies, institutions, agencies, teams",
-                "LOC - Physical locations (mountains, oceans, etc.)",
-                "GPE - Countries, cities, states, political regions",
-                "DATE - Calendar dates, years, centuries",
-                "TIME - Times, durations",
-                "MONEY - Monetary values with currency"
-            ]
-            
-            # User can add custom NER types
-            custom_ner_entities = []
-            if st.checkbox("Add custom NER entities?"):
-                num_custom_ner = st.slider("Number of custom NER entities", 1, 10, 1)
-                for i in range(num_custom_ner):
-                    st.markdown(f"#### Custom Entity {i+1}")
-                    custom_type = st.text_input(f"Entity type {i+1}", key=f"custom_ner_type_{i}")
-                    custom_description = st.text_input(f"Description for {custom_type}", key=f"custom_ner_desc_{i}")
-                    if custom_type and custom_description:
-                        custom_ner_entities.append(f"{custom_type.upper()} - {custom_description}")
-            
-            # Combine built-in and custom NERs
-            all_ner_options = ner_entities + custom_ner_entities
-            
-            selected_entities = st.multiselect(
-                "Select entities to recognize", 
-                all_ner_options,
-                default=ner_entities
-            )
-            
-            # Extract entity type names (before the dash)
-            labels = [entity.split(" - ")[0].strip() for entity in selected_entities]
-
-            if not labels:
-                st.warning("Please select at least one entity type.")
-                labels = ["PERSON"]
-
-            ##########
-        
-            # # Extract just the entity type (before the dash)
-            # labels = [entity.split(" - ")[0] for entity in selected_entities]
-        
-            # if not labels:
-            #     st.warning("Please select at least one entity type")
-            #     labels = ["PERSON"]  # Default if nothing selected
-
-            
-                            
-
-
-    #NNew edit
-            # elif classification_type == "Multi-Class Classification":
-            #     st.write("### Multi-Class Classification Labels")
-            
-            #     default_labels_by_domain = {
-            #         "News": ["Political", "Sports", "Entertainment", "Technology", "Business"],
-            #         "AG News": ["World", "Sports", "Business", "Sci/Tech"],
-            #         "Tourism": ["Accommodation", "Transportation", "Tourist Attractions", 
-            #                     "Food & Dining", "Local Experience", "Adventure Activities",
-            #                     "Wellness & Spa", "Eco-Friendly Practices", "Family-Friendly",
-            #                     "Luxury Tourism"],
-            #         "Restaurant reviews": ["Italian", "French", "American"]
-            #     }
-            #     num_classes = st.slider("Number of classes", 3, 10, 3)
-            
-            #     # Get defaults for selected domain, or empty list
-            #     defaults = default_labels_by_domain.get(domain, [])
-            
-            #     labels = []
-            #     errors = []
-            #     cols = st.columns(3)
-            
-            #     for i in range(num_classes):
-            #         with cols[i % 3]:
-            #             default_value = defaults[i] if i < len(defaults) else ""
-            #             label_input = st.text_input(f"Class {i+1}", default_value)
-            #             normalized_label = label_input.strip().title()
-            
-            #             if not normalized_label:
-            #                 errors.append(f"Class {i+1} name is required.")
-            #             else:
-            #                 labels.append(normalized_label)
-            
-            #     # Check for duplicates (case-insensitive)
-            #     if len(labels) != len(set(labels)):
-                #     errors.append("Labels names must be unique (case-insensitive, normalized to Title Case).")
-    
-                # # Show validation results
-                # if errors:
-                #     for error in errors:
-                #         st.error(error)
-                # else:
-                #     st.success("All Labels names are valid and unique!")
-                # labels_valid = not errors  # Will be True only if there are no label errors
-
-                   
-
-            
-        # else:
-        #     num_classes = st.slider("Number of classes", 3, 23, 3, key="label_num_classes")
-        #     labels = []
-        #     cols = st.columns(3)
-        #     for i in range(num_classes):
-        #         with cols[i % 3]:
-        #             label = st.text_input(f"Class {i+1}", f"Class_{i+1}", key=f"label_class_{i}")
-        #             labels.append(label)
-
-        use_few_shot = st.toggle("Use few-shot examples for labeling")
-        few_shot_examples = []
-        if use_few_shot:
-            num_few_shot = st.slider("Number of few-shot examples", 1, 10, 1)
-            for i in range(num_few_shot):
-                with st.expander(f"Few-shot Example {i+1}"):
-                    content = st.text_area(f"Content", key=f"label_few_shot_content_{i}")
-                    label = st.selectbox(f"Label", labels, key=f"label_few_shot_label_{i}")
-                    if content and label:
-                        few_shot_examples.append(f"{content}\nLabel: {label}")
-
-        num_examples = st.number_input("Number of examples to classify", 1, 100, 1)
-    
-        examples_to_classify = []
-        if num_examples <= 10:
-            for i in range(num_examples):
-                example = st.text_area(f"Example {i+1}", key=f"example_{i}")
-                if example:
-                    examples_to_classify.append(example)
-        else:
-            examples_text = st.text_area(
-                "Enter examples (one per line)",
-                height=300,
-                help="Enter each example on a new line"
-            )
-            if examples_text:
-                examples_to_classify = [ex.strip() for ex in examples_text.split('\n') if ex.strip()]
-                if len(examples_to_classify) > num_examples:
-                    examples_to_classify = examples_to_classify[:num_examples]
-                    
-        #New Wedyan
-        #default_system_role = f"You are a professional {classification_type} expert, your role is to classify the provided text examples for {domain} domain."
-         # System role customization  
-        default_system_role = (f"You are a highly skilled {classification_type} expert."
-        f" Your task is to accurately classify the provided text examples within the {domain} domain."
-        f" Ensure that all classifications are precise, context-aware, and aligned with domain-specific standards and best practices."
-    )
-        system_role = st.text_area("Modify System Role (optional)", 
-                                value=default_system_role,
-                                key="system_role_input")
-        st.session_state['system_role'] = system_role if system_role else default_system_role
-        # Labels initialization
-        #labels = []
-        ####
-
-        user_prompt = st.text_area("User prompt (optional)", key="label_instructions")
-
-        few_shot_text = "\n\n".join(few_shot_examples) if few_shot_examples else ""
-        examples_text = "\n".join([f"{i+1}. {ex}" for i, ex in enumerate(examples_to_classify)])
-    
-        # Customize prompt template based on classification type
-        if classification_type == "Named Entity Recognition (NER)":
-            # label_prompt_template = PromptTemplate(
-            #     input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
-            #     template=(
-            #         "{system_role}\n"
-            #         #"- You are a professional Named Entity Recognition (NER) expert in {domain} domain. Your role is to identify and extract the following entity types: {labels}.\n"
-            #         "- For each text example provided, identify all entities of the requested types.\n"
-            #         "- Use the following entities: {labels}.\n"
-            #         "- Return each example followed by the entities you found in this format: 'Example text.\n \n Entities:\n [ENTITY_TYPE: entity text\n\n, ENTITY_TYPE: entity text\n\n, ...] or [No entities found]'\n"
-            #         "- If no entities of the requested types are found, indicate 'No entities found' in this text.\n"
-            #         "- Be precise about entity boundaries - don't include unnecessary words.\n"
-            #         "- Do not provide any additional information or explanations.\n"
-            #         "- Additional instructions:\n {user_prompt}\n\n"
-            #         "- Use user few-shot examples as guidance if provided:\n{few_shot_examples}\n\n"
-            #         "- Examples to analyze:\n{examples}\n\n"
-            #         "Output:\n"
-            #     )
-            # )
-            #new 22/4/2025
-            # label_prompt_template = PromptTemplate(
-            #     input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
-            #     template=(
-            #         "{system_role}\n"
-            #         "- You are performing Named Entity Recognition (NER) in the domain of {domain}.\n"
-            #         "- Use the following entity types: {labels}.\n\n"
-            #         "### Reasoning Steps:\n"
-            #         "1. Read the example carefully.\n"
-            #         "2. For each named entity mentioned, determine its meaning and role in the sentence.\n"
-            #         "3. Think about the **context**: Is it a physical location (LOC)? A geopolitical region (GPE)? A person (PERSON)?\n"
-            #         "4. Based on the definition of each label, assign the most **specific and correct** label.\n\n"
-            #         "For example:\n"
-            #         "- 'Mount Everest' → LOC (it's a mountain)\n"
-            #         "- 'France' → GPE (it's a country)\n"
-            #         "- 'Microsoft' → ORG\n"
-            #         "- 'John Smith' → PERSON\n\n"
-            #         "- Return each example followed by the entities you found in this format:\n"
-            #         "'Example text.'\nEntities: [ENTITY_TYPE: entity text, ENTITY_TYPE: entity text, ...] or [No entities found]\n"
-            #         "- If no entities of the requested types are found, return 'No entities found'.\n"
-            #         "- Be precise about entity boundaries - don't include extra words.\n"
-            #         "- Do not explain or justify your answers.\n\n"
-            #         "Additional instructions:\n{user_prompt}\n\n"
-            #         "Few-shot examples:\n{few_shot_examples}\n\n"
-            #         "Examples to label:\n{examples}\n"
-            #         "Output:\n"
-            #     )
-            #)
-            # label_prompt_template = PromptTemplate(
-            #     input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
-            #     template=(
-            #         "{system_role}\n"
-            #         "- You are an expert at Named Entity Recognition (NER) for domain: {domain}.\n"
-            #         "- Use these entity types: {labels}.\n\n"
-            #         "### Output Format:\n"
-            #         # "Return each example followed by the entities you found in this format: 'Example text.\n Entities:\n [ENTITY_TYPE: entity text\n\"
-            #         "Return each example followed by the entities you found in this format: 'Example text.\n 'Entity types:\n "Then group the entities under each label like this:\n" "
-            #         #"Then Start with this line exactly: 'Entity types\n'\n"
-            #         #"Then group the entities under each label like this:\n"
-            #         "\n PERSON – Angela Merkel, John Smith\n\n"
-            #         "\ ORG – Google, United Nations\n\n"
-            #         "\n DATE – January 1st, 2023\n\n"
-            #         "\n ... and so on.\n\n"
-            #         "If entity {labels} not found, do not write it in your response\n"
-            #         "- Do NOT output them inline after the text.\n"
-            #         "- Do NOT repeat the sentence.\n"
-            #         "- If no entities are found for a type, skip it.\n"
-            #         "- Keep the format consistent.\n\n"
-            #         "User Instructions:\n{user_prompt}\n\n"
-            #         "Few-shot Examples:\n{few_shot_examples}\n\n"
-            #         "Examples to analyze:\n{examples}"
-            #       )
-            # )
-
-            
-            label_prompt_template = PromptTemplate(
-                input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
-                template=(
-                    "{system_role}\n"
-                    "- You are an expert at Named Entity Recognition (NER) for domain: {domain}.\n"
-                    "- Use these entity types: {labels}.\n\n"
-                    "### Output Format:\n"
-                    "Return each example followed by the entities you found in this format:\n"
-                    "'Example text.\nEntity types:\n"
-                    "Then group the entities under each label like this:\n"
-                    "\nPERSON – Angela Merkel, John Smith\n"
-                    "ORG – Google, United Nations\n"
-                    "DATE – January 1st, 2023\n"
-                    "... and so on.\n\n"
-                    "Each new entities group should be in a new line.\n"
-                    "If entity type {labels} is not found, do not write it in your response.\n"
-                    "- Do NOT output them inline after the text.\n"
-                    "- Do NOT repeat the sentence.\n"
-                    "- If no entities are found for a type, skip it.\n"
-                    "- Keep the format consistent.\n\n"
-                    "User Instructions:\n{user_prompt}\n\n"
-                    "Few-shot Examples:\n{few_shot_examples}\n\n"
-                    "Examples to analyze:\n{examples}"
-                )
-            )
-
-#######
-        else:
-                label_prompt_template = PromptTemplate(
-
-                input_variables=["system_role", "classification_type", "labels", "few_shot_examples", "examples","domain", "user_prompt"],
-                template=(
-                    #"- Let'\s think step by step:"
-                    "{system_role}\n"
-                   # "- You are a professional {classification_type} expert in {domain} domain. Your role is to classify the following examples using these labels: {labels}.\n"
-                    "- Use the following instructions:\n"
-                    "- Use the following labels: {labels}.\n"
-                    "- Return the classified text followed by the label in this format: 'text. Label: [label]'\n"
-                    "- Do not provide any additional information or explanations\n"
-                    "- User prompt:\n {user_prompt}\n\n"
-                    "- Use user provided examples as guidence in the classification process:\n\n {few_shot_examples}\n"
-                    "- Examples to classify:\n{examples}\n\n"
-                    "- Think step by step then classify the examples"
-                    #"Output:\n"
-                ))
-            
-        # Check if few_shot_examples is already a formatted string
-           # Check if few_shot_examples is already a formatted string
-        if isinstance(few_shot_examples, str):
-            formatted_few_shot = few_shot_examples
-        # If it's a list of already formatted strings
-        elif isinstance(few_shot_examples, list) and all(isinstance(ex, str) for ex in few_shot_examples):
-            formatted_few_shot = "\n".join(few_shot_examples)
-        # If it's a list of dictionaries with 'content' and 'label' keys
-        elif isinstance(few_shot_examples, list) and all(isinstance(ex, dict) and 'content' in ex and 'label' in ex for ex in few_shot_examples):
-            formatted_few_shot = "\n".join([f"{ex['content']}\nLabel: {ex['label']}" for ex in few_shot_examples])
-        else:
-            formatted_few_shot = ""
-# #new 22/4/2025
-#         few_shot_examples = [
-#             {"content": "Mount Everest is the tallest mountain in the world.", "label": "LOC: Mount Everest"},
-#             {"content": "The President of the United States visited Paris last summer.", "label": "GPE: United States, GPE: Paris"},
-#             {"content": "Amazon is expanding its offices in Berlin.", "label": "ORG: Amazon, GPE: Berlin"},
-#             {"content": "J.K. Rowling wrote the Harry Potter books.", "label": "PERSON: J.K. Rowling"},
-#             {"content": "Apple was founded in California in 1976.", "label": "ORG: Apple, GPE: California, DATE: 1976"},
-#             {"content": "The Nile is the longest river in Africa.", "label": "LOC: Nile, GPE: Africa"},
-#             {"content": "He arrived at 3 PM for the meeting.", "label": "TIME: 3 PM"},
-#             {"content": "She bought the dress for $200.", "label": "MONEY: $200"},
-#             {"content": "The event is scheduled for July 4th.", "label": "DATE: July 4th"},
-#             {"content": "The World Health Organization is headquartered in Geneva.", "label": "ORG: World Health Organization, GPE: Geneva"}
-#         ]
-# ###########
-# new 22/4/2025
-        #formatted_few_shot = "\n".join([f"{ex['content']}\nEntities: [{ex['label']}]" for ex in few_shot_examples])
-        formatted_few_shot = "\n\n".join([f"{ex['content']}\n\nEntity types\n{ex['label']}" for ex in few_shot_examples])
-
-        ###########
-        system_prompt = label_prompt_template.format(
-            system_role=st.session_state['system_role'],
-            classification_type=classification_type,
-            domain=domain,
-            examples="\n".join(examples_to_classify),
-            labels=", ".join(labels),
-            user_prompt=user_prompt,
-            few_shot_examples=formatted_few_shot
-        )
-
-        # Step 2: Store the system_prompt in st.session_state
-        st.session_state['system_prompt'] = system_prompt           
-#::contentReference[oaicite:0]{index=0}
-        st.write("System Prompt:")
-        #st.code(system_prompt)
-        #st.code(st.session_state['system_prompt'])
-        st.text_area("System Prompt", value=st.session_state['system_prompt'], height=300, max_chars=None, key=None, help=None, disabled=True)
-            
-        
-
-        if st.button("🏷️ Label Data"):
-            if examples_to_classify:
-                with st.spinner("Labeling data..."):
-                    #Generate the system prompt based on classification type
-                    if classification_type == "Named Entity Recognition (NER)":
-                        system_prompt = label_prompt_template.format(
-                            system_role=st.session_state['system_role'],
-                            labels=", ".join(labels),
-                            domain = domain,
-                            few_shot_examples=few_shot_text,
-                            examples=examples_text,
-                            user_prompt=user_prompt
-                    #new 
-                            #'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                       )
-                    # if classification_type == "Named Entity Recognition (NER)":
-                    #     # Step 1: Split the full response by example
-                    #     raw_outputs = [block.strip() for block in response.strip().split("Entity types") if block.strip()]
-                    #     inputs = [ex.strip() for ex in examples_to_classify]
-                    
-                    #     # Step 2: Match inputs with NER output blocks
-                    #     labeled_examples = []
-                    #     for i, (text, output_block) in enumerate(zip(inputs, raw_outputs)):
-                    #         labeled_examples.append({
-                    #             'text': text,
-                    #             'entities': f"Entity types\n{output_block.strip()}",
-                    #             'system_prompt': st.session_state.system_prompt,
-                    #             'system_role': st.session_state.system_role,
-                    #             'task_type': 'Named Entity Recognition (NER)',
-                    #             'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                    #         })
-                    
-                    # if classification_type == "Named Entity Recognition (NER)":
-                    #     # Step 1: Split the full response by example
-                    #     raw_outputs = [block.strip() for block in response.strip().split("Entity types") if block.strip()]
-                    #     inputs = [ex.strip() for ex in examples_to_classify]
-                    
-                    #     # Step 2: Match inputs with NER output blocks
-                    #     labeled_examples = []
-                    #     for i, (text, output_block) in enumerate(zip(inputs, raw_outputs)):
-                    #         labeled_examples.append({
-                    #             'text': text,
-                    #             'entities': f"Entity types\n{output_block.strip()}",
-                    #             'system_prompt': st.session_state.system_prompt,
-                    #             'system_role': st.session_state.system_role,
-                    #             'task_type': 'Named Entity Recognition (NER)',
-                    #             'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                    #         })
-
-        
-                    # import re
-
-                    # if classification_type == "Named Entity Recognition (NER)":
-                    #     # Use regex to split on "Entity types" while keeping it attached to each block
-                    #     blocks = re.split(r"(Entity types)", response.strip())
-                        
-                    #     # Recombine 'Entity types' with each block after splitting
-                    #     raw_outputs = [
-                    #         (blocks[i] + blocks[i+1]).strip()
-                    #         for i in range(1, len(blocks) - 1, 2)
-                    #     ]
-                        
-                    #     inputs = [ex.strip() for ex in examples_to_classify]
-                    
-                    #     labeled_examples = []
-                    #     for i, (text, output_block) in enumerate(zip(inputs, raw_outputs)):
-                    #         labeled_examples.append({
-                    #             'text': text,
-                    #             'entities': output_block,
-                    #             'system_prompt': st.session_state.system_prompt,
-                    #             'system_role': st.session_state.system_role,
-                    #             'task_type': 'Named Entity Recognition (NER)',
-                    #             'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                    #         })
-
-
-                    else:
-                        system_prompt = label_prompt_template.format(
-                            classification_type=classification_type,
-                            system_role=st.session_state['system_role'],
-                            domain = domain,
-                            labels=", ".join(labels),
-                            few_shot_examples=few_shot_text,
-                            examples=examples_text,
-                            user_prompt=user_prompt
-                        )
-                    try:
-                        stream = client.chat.completions.create(
-                            model=selected_model,
-                            messages=[{"role": "system", "content": system_prompt}],
-                            temperature=temperature,
-                            stream=True,
-                            #max_tokens=20000,
-                            max_tokens=4000,
-                            top_p = 0.9,
-                          
-                        )
-                        #new 24 March
-                        # Append user message
-                        st.session_state.messages.append({"role": "user", "content": system_prompt})
-                        #################
-                        response = st.write_stream(stream)
-                        st.session_state.messages.append({"role": "assistant", "content": response})
-                         # Display the labeled examples
-                        #    # Optional: If you want to add it as a chat-style message log
-                        # preview_str = st.session_state.labeled_preview.to_markdown(index=False)
-                        # st.session_state.messages.append({"role": "assistant", "content": f"Here is a preview of the labeled examples:\n\n{preview_str}"})
-                                                                                                    
-
-                        # # Stream response and append assistant message
-                        # #14/4/2024
-                        # response = st.write_stream(stream)
-                        # st.session_state.messages.append({"role": "assistant", "content": response})
-
-                          # Initialize session state variables if they don't exist
-                        if 'system_prompt' not in st.session_state:
-                            st.session_state.system_prompt = system_prompt
-                        
-                        if 'response' not in st.session_state:
-                            st.session_state.response = response
-                        
-                        if 'generated_examples' not in st.session_state:
-                            st.session_state.generated_examples = []
-                        
-                        if 'generated_examples_csv' not in st.session_state:
-                            st.session_state.generated_examples_csv = None
-                        
-                        if 'generated_examples_json' not in st.session_state:
-                            st.session_state.generated_examples_json = None
-                    
-
-                      
-                    
-                        # Save labeled examples to CSV
-                        #new 14/4/2025
-                        #labeled_examples = []
-                        # if classification_type == "Named Entity Recognition (NER)":
-                        #     labeled_examples = []
-                        #     for line in response.split('\n'):
-                        #         if line.strip():
-                        #             parts = line.rsplit('Entities:', 1)
-                        #             if len(parts) == 2:
-                        #                 text = parts[0].strip()
-                        #                 entities = parts[1].strip()
-                        #                 if text and entities:
-                        #                     labeled_examples.append({
-                        #                         'text': text,
-                        #                         'entities': entities,
-                        #                         'system_prompt': st.session_state.system_prompt,
-                        #                         'system_role': st.session_state.system_role,
-                        #                         'task_type': 'Named Entity Recognition (NER)',
-                        #                         'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                        #                     })
-
-                                            #new 22/4/2025
-                        labeled_examples = []
-                        if classification_type == "Named Entity Recognition (NER)":
-                            labeled_examples = [{
-                                'ner_output': response.strip(),
-                                'system_prompt': st.session_state.system_prompt,
-                                'system_role': st.session_state.system_role,
-                                'task_type': 'Named Entity Recognition (NER)',
-                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',
-                            }]
-
-                                            ######
-                       
-                                            
-                        else:
-                            labeled_examples = []
-                            for line in response.split('\n'):
-                                if line.strip():
-                                    parts = line.rsplit('Label:', 1)
-                                    if len(parts) == 2:
-                                        text = parts[0].strip()
-                                        label = parts[1].strip()
-                                        if text and label:
-                                            labeled_examples.append({
-                                                'text': text,
-                                                'label': label,
-                                                'system_prompt': st.session_state.system_prompt,
-                                                'system_role': st.session_state.system_role,
-                                                'task_type': 'Data Labeling',
-                                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',  
-                                            })
-                       # Save and provide download options
-                        if labeled_examples:
-                            # Update session state
-                            st.session_state.labeled_examples = labeled_examples
-                        
-                            # Convert to CSV and JSON
-                            df = pd.DataFrame(labeled_examples)
-                            #new 22/4/2025
-                            # CSV
-                            st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')
-                            
-                            # JSON
-                            st.session_state.labeled_examples_json = json.dumps({
-                                "metadata": {
-                                    "domain": domain,
-                                    "labels": labels,
-                                    "used_few_shot": use_few_shot,
-                                    "task_type": "Named Entity Recognition (NER)",
-                                    "timestamp": datetime.now().isoformat()
-                                },
-                                "examples": labeled_examples
-                            }, indent=2).encode('utf-8')
-
-                            ############
-                            # CSV
-                            # st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')
-                            
-                            # # JSON
-                            # st.session_state.labeled_examples_json = json.dumps({
-                            #     "metadata": {
-                            #         "domain": domain,
-                            #         "labels": labels,
-                            #         "used_few_shot": use_few_shot,
-                            #         "task_type": "Named Entity Recognition (NER)",
-                            #         "timestamp": datetime.now().isoformat()
-                            #     },
-                            #     "examples": labeled_examples
-                            # }, indent=2).encode('utf-8')
-                            
-                                                        ########
-                            # st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')
-                            # st.session_state.labeled_examples_json = json.dumps(labeled_examples, indent=2).encode('utf-8')
-                        
-                            # Download buttons
-                            st.download_button(
-                                "📥 Download Labeled Examples (CSV)",
-                                st.session_state.labeled_examples_csv,
-                                "labeled_examples.csv",
-                                "text/csv",
-                                key='download-labeled-csv'
-                            )
-                        
-                            st.markdown("""
-                            <div style='text-align: left; margin:15px 0; font-weight: 600; color: #666;'>. . . . . .         or</div>
-                            """, unsafe_allow_html=True)
-                        
-                            st.download_button(
-                                "📥 Download Labeled Examples (JSON)",
-                                st.session_state.labeled_examples_json,
-                                "labeled_examples.json",
-                                "application/json",
-                                key='download-labeled-json'
-                            )
-                            # Display the labeled examples
-                            st.markdown("##### 📋 Labeled Examples Preview")
-                            st.dataframe(df, use_container_width=True)
-                            # Display section
-                            #st.markdown("### 📋 Labeled Examples Preview")
-                            #st.dataframe(st.session_state.labeled_preview, use_container_width=True)
-
-                                                                            
-                                                                        
-                        # if labeled_examples:
-                        #     df = pd.DataFrame(labeled_examples)
-                        #     csv = df.to_csv(index=False).encode('utf-8')
-                        #     st.download_button(
-                        #         "📥 Download Labeled Examples",
-                        #         csv,
-                        #         "labeled_examples.csv",
-                        #         "text/csv",
-                        #         key='download-labeled-csv'
-                        #     )
-                        # # Add space and center the "or"
-                        # st.markdown("""
-                        # <div style='text-align: left; margin:15px 0; font-weight: 600; color: #666;'>. . . . . .         or</div>
-                        # """, unsafe_allow_html=True)
-                        
-                        # if labeled_examples:
-                        #     df = pd.DataFrame(labeled_examples)
-                        #     csv = df.to_csv(index=False).encode('utf-8')
-                        #     st.download_button(
-                        #         "📥 Download Labeled Examples",
-                        #         csv,
-                        #         "labeled_examples.json",
-                        #         "text/json",
-                        #         key='download-labeled-JSON'
-                        #     )
-                            
-                        # Add follow-up interaction options
-                        #st.markdown("---")
-                        #follow_up = st.radio(
-                            #"What would you like to do next?",
-                             #["Label more data", "Data Generation"],
-                           # key="labeling_follow_up"
-                      #  )
-                    
-                        if st.button("Continue"):
-                            if follow_up == "Label more data":
-                                st.session_state.examples_to_classify = []
-                                st.experimental_rerun()
-                            elif follow_up == "Data Generation":
-                                st.session_state.task_choice = "Data Labeling"
-                                st.experimental_rerun()
-                            
-                    except Exception as e:
-                        st.error("An error occurred during labeling.")
-                        st.error(f"Details: {e}")
-            else:
-                st.warning("Please enter at least one example to classify.")
-
-    #st.session_state.messages.append({"role": "assistant", "content": response})
-       
-        
-                   
-
-# Footer
-st.markdown("---")
-st.markdown(
-    """
-    <div style='text-align: center'>
-        <p>Made with ❤️ by Wedyan AlSakran 2025</p>
-    </div>
-    """, 
-    unsafe_allow_html=True
-)