Update app.py

app.py

@@ -1,52 +1,665 @@
+Ok on the following make my model rather than llama model here
+
 import gradio as gr
-from huggingface_hub import InferenceClient
 import os
+import PyPDF2
+import logging
+import torch
+import threading
+import time
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+    StoppingCriteria,
+    StoppingCriteriaList,
+)
+from transformers import logging as hf_logging
+import spaces
+from llama_index.core import (
+    StorageContext,
+    VectorStoreIndex,
+    load_index_from_storage,
+    Document as LlamaDocument,
+)
+from llama_index.core import Settings
+from llama_index.core.node_parser import (
+    HierarchicalNodeParser,
+    get_leaf_nodes,
+    get_root_nodes,
+)
+from llama_index.core.retrievers import AutoMergingRetriever
+from llama_index.core.storage.docstore import SimpleDocumentStore
+from llama_index.llms.huggingface import HuggingFaceLLM
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from tqdm import tqdm
 
-# 🔹 Hugging Face Credentials
-HF_REPO = "Futuresony/future_ai_12_10_2024.gguf"
-HF_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
-
-client = InferenceClient(HF_REPO, token=HF_TOKEN)
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+hf_logging.set_verbosity_error()
 
-def format_alpaca_prompt(user_input, system_prompt, history):
-    """Formats input in Alpaca/LLaMA style"""
-    history_str = "\n".join([f"### Instruction:\n{h[0]}\n### Response:\n{h[1]}" for h in history])
-    prompt = f"""{system_prompt}
-{history_str}
+MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN not found in environment variables")
 
-### Instruction:
-{user_input}
+# --- UI Settings ---
+TITLE = "<h1 style='text-align:center; margin-bottom: 20px;'>Local Thinking RAG: Llama 3.1 8B</h1>"
+DISCORD_BADGE = """<p style="text-align:center; margin-top: -10px;">
+<a href="https://discord.gg/openfreeai" target="_blank">
+  <img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=white&style=for-the-badge" alt="badge">
+</a>
+</p>
+"""
 
-### Response:
+CSS = """
+.upload-section {
+    max-width: 400px;
+    margin: 0 auto;
+    padding: 10px;
+    border: 2px dashed #ccc;
+    border-radius: 10px;
+}
+.upload-button {
+    background: #34c759 !important;
+    color: white !important;
+    border-radius: 25px !important;
+}
+.chatbot-container {
+    margin-top: 20px;
+}
+.status-output {
+    margin-top: 10px;
+    font-size: 14px;
+}
+.processing-info {
+    margin-top: 5px;
+    font-size: 12px;
+    color: #666;
+}
+.info-container {
+    margin-top: 10px;
+    padding: 10px;
+    border-radius: 5px;
+}
+.file-list {
+    margin-top: 0;
+    max-height: 200px;
+    overflow-y: auto;
+    padding: 5px;
+    border: 1px solid #eee;
+    border-radius: 5px;
+}
+.stats-box {
+    margin-top: 10px;
+    padding: 10px;
+    border-radius: 5px;
+    font-size: 12px;
+}
+.submit-btn {
+    background: #1a73e8 !important;
+    color: white !important;
+    border-radius: 25px !important;
+    margin-left: 10px;
+    padding: 5px 10px;
+    font-size: 16px;
+}
+.input-row {
+    display: flex;
+    align-items: center;
+}
+@media (min-width: 768px) {
+    .main-container {
+        display: flex;
+        justify-content: space-between;
+        gap: 20px;
+    }
+    .upload-section {
+        flex: 1;
+        max-width: 300px;
+    }
+    .chatbot-container {
+        flex: 2;
+        margin-top: 0;
+    }
+}
 """
-    return prompt
 
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    formatted_prompt = format_alpaca_prompt(message, system_message, history)
+global_model = None
+global_tokenizer = None
+global_file_info = {}
 
-    response = client.text_generation(
-        formatted_prompt,
-        max_new_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
+def initialize_model_and_tokenizer():
+    global global_model, global_tokenizer
+    if global_model is None or global_tokenizer is None:
+        logger.info("Initializing model and tokenizer...")
+        global_tokenizer = AutoTokenizer.from_pretrained(MODEL, token=HF_TOKEN)
+        global_model = AutoModelForCausalLM.from_pretrained(
+            MODEL,
+            device_map="auto",
+            trust_remote_code=True,
+            token=HF_TOKEN,
+            torch_dtype=torch.float16
+        )
+        logger.info("Model and tokenizer initialized successfully")
+
+def get_llm(temperature=0.7, max_new_tokens=256, top_p=0.95, top_k=50):
+    global global_model, global_tokenizer
+    if global_model is None or global_tokenizer is None:
+        initialize_model_and_tokenizer()
+    
+    return HuggingFaceLLM(
+        context_window=4096,
+        max_new_tokens=max_new_tokens,
+        tokenizer=global_tokenizer,
+        model=global_model,
+        generate_kwargs={
+            "do_sample": True,
+            "temperature": temperature,
+            "top_k": top_k,
+            "top_p": top_p
+        }
     )
 
-    # ✅ Extract only the response
-    cleaned_response = response.split("### Response:")[-1].strip()
+def extract_text_from_document(file):
+    file_name = file.name
+    file_extension = os.path.splitext(file_name)[1].lower()
     
-    history.append((message, cleaned_response))  # ✅ Update history with the new message and response
+    if file_extension == '.txt':
+        text = file.read().decode('utf-8')
+        return text, len(text.split()), None
+    elif file_extension == '.pdf':
+        pdf_reader = PyPDF2.PdfReader(file)
+        text = "\n\n".join(page.extract_text() for page in pdf_reader.pages)
+        return text, len(text.split()), None
+    else:
+        return None, 0, ValueError(f"Unsupported file format: {file_extension}")
+
+@spaces.GPU()
+def create_or_update_index(files, request: gr.Request):
+    global global_file_info
+    
+    if not files:
+        return "Please provide files.", ""
+    
+    start_time = time.time()
+    user_id = request.session_hash
+    save_dir = f"./{user_id}_index"
+    # Initialize LlamaIndex modules
+    llm = get_llm()
+    embed_model = HuggingFaceEmbedding(model_name=EMBEDDING_MODEL, token=HF_TOKEN)
+    Settings.llm = llm
+    Settings.embed_model = embed_model
+    file_stats = []
+    new_documents = []
     
-    yield cleaned_response  # ✅ Output only the answer
+    for file in tqdm(files, desc="Processing files"):
+        file_basename = os.path.basename(file.name)
+        text, word_count, error = extract_text_from_document(file)
+        if error:
+            logger.error(f"Error processing file {file_basename}: {str(error)}")
+            file_stats.append({
+                "name": file_basename,
+                "words": 0,
+                "status": f"error: {str(error)}"
+            })
+            continue
+        
+        doc = LlamaDocument(
+            text=text,
+            metadata={
+                "file_name": file_basename,
+                "word_count": word_count,
+                "source": "user_upload"
+            }
+        )
+        new_documents.append(doc)
+        
+        file_stats.append({
+            "name": file_basename,
+            "words": word_count,
+            "status": "processed"
+        })
+        
+        global_file_info[file_basename] = {
+            "word_count": word_count,
+            "processed_at": time.time()
+        }
+    
+    node_parser = HierarchicalNodeParser.from_defaults(
+        chunk_sizes=[2048, 512, 128],  
+        chunk_overlap=20         
+    )
+    logger.info(f"Parsing {len(new_documents)} documents into hierarchical nodes")
+    new_nodes = node_parser.get_nodes_from_documents(new_documents)
+    new_leaf_nodes = get_leaf_nodes(new_nodes)
+    new_root_nodes = get_root_nodes(new_nodes)
+    logger.info(f"Generated {len(new_nodes)} total nodes ({len(new_root_nodes)} root, {len(new_leaf_nodes)} leaf)")
+    
+    if os.path.exists(save_dir):
+        logger.info(f"Loading existing index from {save_dir}")
+        storage_context = StorageContext.from_defaults(persist_dir=save_dir)
+        index = load_index_from_storage(storage_context, settings=Settings)
+        docstore = storage_context.docstore
+        
+        docstore.add_documents(new_nodes)
+        for node in tqdm(new_leaf_nodes, desc="Adding leaf nodes to index"):
+            index.insert_nodes([node])
+            
+        total_docs = len(docstore.docs)
+        logger.info(f"Updated index with {len(new_nodes)} new nodes from {len(new_documents)} files")
+    else:
+        logger.info("Creating new index")
+        docstore = SimpleDocumentStore()
+        storage_context = StorageContext.from_defaults(docstore=docstore)
+        docstore.add_documents(new_nodes)
+        
+        index = VectorStoreIndex(
+            new_leaf_nodes, 
+            storage_context=storage_context, 
+            settings=Settings
+        )
+        total_docs = len(new_documents)
+        logger.info(f"Created new index with {len(new_nodes)} nodes from {len(new_documents)} files")
+    
+    index.storage_context.persist(persist_dir=save_dir)
+    # custom outputs after processing files
+    file_list_html = "<div class='file-list'>"
+    for stat in file_stats:
+        status_color = "#4CAF50" if stat["status"] == "processed" else "#f44336"
+        file_list_html += f"<div><span style='color:{status_color}'>●</span> {stat['name']} - {stat['words']} words</div>"
+    file_list_html += "</div>"
+    processing_time = time.time() - start_time
+    stats_output = f"<div class='stats-box'>"
+    stats_output += f"✓ Processed {len(files)} files in {processing_time:.2f} seconds<br>"
+    stats_output += f"✓ Created {len(new_nodes)} nodes ({len(new_leaf_nodes)} leaf nodes)<br>"
+    stats_output += f"✓ Total documents in index: {total_docs}<br>"
+    stats_output += f"✓ Index saved to: {save_dir}<br>"
+    stats_output += "</div>"
+    output_container = f"<div class='info-container'>"
+    output_container += file_list_html
+    output_container += stats_output
+    output_container += "</div>"
+    return f"Successfully indexed {len(files)} files.", output_container
 
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=250, value=128, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.9, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.99, step=0.01, label="Top-p (nucleus sampling)"),
-    ],
-)
+@spaces.GPU()
+def create_or_update_index(files, request: gr.Request):
+    global global_file_info
+    
+    if not files:
+        return "Please provide files.", ""
+    
+    start_time = time.time()
+    user_id = request.session_hash
+    save_dir = f"./{user_id}_index"
+    # Initialize LlamaIndex modules
+    llm = get_llm()
+    embed_model = HuggingFaceEmbedding(model_name=EMBEDDING_MODEL, token=HF_TOKEN)
+    Settings.llm = llm
+    Settings.embed_model = embed_model
+    file_stats = []
+    new_documents = []
+    
+    for file in tqdm(files, desc="Processing files"):
+        file_basename = os.path.basename(file.name)
+        text, word_count, error = extract_text_from_document(file)
+        if error:
+            logger.error(f"Error processing file {file_basename}: {str(error)}")
+            file_stats.append({
+                "name": file_basename,
+                "words": 0,
+                "status": f"error: {str(error)}"
+            })
+            continue
+        
+        doc = LlamaDocument(
+            text=text,
+            metadata={
+                "file_name": file_basename,
+                "word_count": word_count,
+                "source": "user_upload"
+            }
+        )
+        new_documents.append(doc)
+        
+        file_stats.append({
+            "name": file_basename,
+            "words": word_count,
+            "status": "processed"
+        })
+        
+        global_file_info[file_basename] = {
+            "word_count": word_count,
+            "processed_at": time.time()
+        }
+    
+    node_parser = HierarchicalNodeParser.from_defaults(
+        chunk_sizes=[2048, 512, 128],  
+        chunk_overlap=20         
+    )
+    logger.info(f"Parsing {len(new_documents)} documents into hierarchical nodes")
+    new_nodes = node_parser.get_nodes_from_documents(new_documents)
+    new_leaf_nodes = get_leaf_nodes(new_nodes)
+    new_root_nodes = get_root_nodes(new_nodes)
+    logger.info(f"Generated {len(new_nodes)} total nodes ({len(new_root_nodes)} root, {len(new_leaf_nodes)} leaf)")
+    
+    if os.path.exists(save_dir):
+        logger.info(f"Loading existing index from {save_dir}")
+        storage_context = StorageContext.from_defaults(persist_dir=save_dir)
+        index = load_index_from_storage(storage_context, settings=Settings)
+        docstore = storage_context.docstore
+        
+        docstore.add_documents(new_nodes)
+        for node in tqdm(new_leaf_nodes, desc="Adding leaf nodes to index"):
+            index.insert_nodes([node])
+            
+        total_docs = len(docstore.docs)
+        logger.info(f"Updated index with {len(new_nodes)} new nodes from {len(new_documents)} files")
+    else:
+        logger.info("Creating new index")
+        docstore = SimpleDocumentStore()
+        storage_context = StorageContext.from_defaults(docstore=docstore)
+        docstore.add_documents(new_nodes)
+        
+        index = VectorStoreIndex(
+            new_leaf_nodes, 
+            storage_context=storage_context, 
+            settings=Settings
+        )
+        total_docs = len(new_documents)
+        logger.info(f"Created new index with {len(new_nodes)} nodes from {len(new_documents)} files")
+    
+    index.storage_context.persist(persist_dir=save_dir)
+    # custom outputs after processing files
+    file_list_html = "<div class='file-list'>"
+    for stat in file_stats:
+        status_color = "#4CAF50" if stat["status"] == "processed" else "#f44336"
+        file_list_html += f"<div><span style='color:{status_color}'>●</span> {stat['name']} - {stat['words']} words</div>"
+    file_list_html += "</div>"
+    processing_time = time.time() - start_time
+    stats_output = f"<div class='stats-box'>"
+    stats_output += f"✓ Processed {len(files)} files in {processing_time:.2f} seconds<br>"
+    stats_output += f"✓ Created {len(new_nodes)} nodes ({len(new_leaf_nodes)} leaf nodes)<br>"
+    stats_output += f"✓ Total documents in index: {total_docs}<br>"
+    stats_output += f"✓ Index saved to: {save_dir}<br>"
+    stats_output += "</div>"
+    output_container = f"<div class='info-container'>"
+    output_container += file_list_html
+    output_container += stats_output
+    output_container += "</div>"
+    return f"Successfully indexed {len(files)} files.", output_container
+
+@spaces.GPU()
+def stream_chat(
+    message: str,
+    history: list,
+    system_prompt: str,
+    temperature: float,
+    max_new_tokens: int,
+    top_p: float,
+    top_k: int,
+    penalty: float,
+    retriever_k: int,
+    merge_threshold: float,
+    request: gr.Request
+):
+    if not request:
+        yield history + [{"role": "assistant", "content": "Session initialization failed. Please refresh the page."}]
+        return
+    user_id = request.session_hash
+    index_dir = f"./{user_id}_index"
+    if not os.path.exists(index_dir):
+        yield history + [{"role": "assistant", "content": "Please upload documents first."}]
+        return
+
+    max_new_tokens = int(max_new_tokens) if isinstance(max_new_tokens, (int, float)) else 1024
+    temperature = float(temperature) if isinstance(temperature, (int, float)) else 0.9  
+    top_p = float(top_p) if isinstance(top_p, (int, float)) else 0.95  
+    top_k = int(top_k) if isinstance(top_k, (int, float)) else 50  
+    penalty = float(penalty) if isinstance(penalty, (int, float)) else 1.2
+    retriever_k = int(retriever_k) if isinstance(retriever_k, (int, float)) else 15
+    merge_threshold = float(merge_threshold) if isinstance(merge_threshold, (int, float)) else 0.5
+    llm = get_llm(temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k)
+    embed_model = HuggingFaceEmbedding(model_name=EMBEDDING_MODEL, token=HF_TOKEN)
+    Settings.llm = llm
+    Settings.embed_model = embed_model
+    storage_context = StorageContext.from_defaults(persist_dir=index_dir)
+    index = load_index_from_storage(storage_context, settings=Settings)
+    base_retriever = index.as_retriever(similarity_top_k=retriever_k)
+    auto_merging_retriever = AutoMergingRetriever(
+        base_retriever,
+        storage_context=storage_context,
+        simple_ratio_thresh=merge_threshold, 
+        verbose=True
+    )
+    logger.info(f"Query: {message}")
+    retrieval_start = time.time()
+    base_nodes = base_retriever.retrieve(message)
+    logger.info(f"Retrieved {len(base_nodes)} base nodes in {time.time() - retrieval_start:.2f}s")
+    base_file_sources = {}
+    for node in base_nodes:
+        if hasattr(node.node, 'metadata') and 'file_name' in node.node.metadata:
+            file_name = node.node.metadata['file_name']
+            if file_name not in base_file_sources:
+                base_file_sources[file_name] = 0
+            base_file_sources[file_name] += 1
+    logger.info(f"Base retrieval file distribution: {base_file_sources}")
+    merging_start = time.time()
+    merged_nodes = auto_merging_retriever.retrieve(message)
+    logger.info(f"Retrieved {len(merged_nodes)} merged nodes in {time.time() - merging_start:.2f}s")
+    merged_file_sources = {}
+    for node in merged_nodes:
+        if hasattr(node.node, 'metadata') and 'file_name' in node.node.metadata:
+            file_name = node.node.metadata['file_name']
+            if file_name not in merged_file_sources:
+                merged_file_sources[file_name] = 0
+            merged_file_sources[file_name] += 1
+    logger.info(f"Merged retrieval file distribution: {merged_file_sources}")
+    context = "\n\n".join([n.node.text for n in merged_nodes])
+    source_info = ""
+    if merged_file_sources:
+        source_info = "\n\nRetrieved information from files: " + ", ".join(merged_file_sources.keys())
+    formatted_system_prompt = f"{system_prompt}\n\nDocument Context:\n{context}{source_info}"
+    messages = [{"role": "system", "content": formatted_system_prompt}]
+    for entry in history:
+        messages.append(entry)
+    messages.append({"role": "user", "content": message})
+    prompt = global_tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    stop_event = threading.Event()
+    class StopOnEvent(StoppingCriteria):
+        def __init__(self, stop_event):
+            super().__init__()
+            self.stop_event = stop_event
+
+        def __call__(self, input_ids, scores, **kwargs):
+            return self.stop_event.is_set()
+    stopping_criteria = StoppingCriteriaList([StopOnEvent(stop_event)])
+    streamer = TextIteratorStreamer(
+        global_tokenizer,
+        skip_prompt=True,
+        skip_special_tokens=True
+    )
+    inputs = global_tokenizer(prompt, return_tensors="pt").to(global_model.device)
+    generation_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=penalty,
+        do_sample=True,
+        stopping_criteria=stopping_criteria
+    )
+    thread = threading.Thread(target=global_model.generate, kwargs=generation_kwargs)
+    thread.start()
+    updated_history = history + [
+        {"role": "user", "content": message},
+        {"role": "assistant", "content": ""}
+    ]
+    yield updated_history
+    partial_response = ""
+    try:
+        for new_text in streamer:
+            partial_response += new_text
+            updated_history[-1]["content"] = partial_response
+            yield updated_history
+        yield updated_history
+    except GeneratorExit:
+        stop_event.set()
+        thread.join()
+        raise
+
+def create_demo():
+    with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
+        # Title
+        gr.HTML(TITLE)
+        # Discord badge immediately under the title
+        gr.HTML(DISCORD_BADGE)
+
+        with gr.Row(elem_classes="main-container"):
+            with gr.Column(elem_classes="upload-section"):
+                file_upload = gr.File(
+                    file_count="multiple",
+                    label="Drag & Drop PDF/TXT Files Here",
+                    file_types=[".pdf", ".txt"],
+                    elem_id="file-upload"
+                )
+                upload_button = gr.Button("Upload & Index", elem_classes="upload-button")
+                status_output = gr.Textbox(
+                    label="Status",
+                    placeholder="Upload files to start...",
+                    interactive=False
+                )
+                file_info_output = gr.HTML(
+                    label="File Information",
+                    elem_classes="processing-info"
+                )
+                upload_button.click(
+                    fn=create_or_update_index,
+                    inputs=[file_upload],
+                    outputs=[status_output, file_info_output]
+                )
+            
+            with gr.Column(elem_classes="chatbot-container"):
+                chatbot = gr.Chatbot(
+                    height=500,
+                    placeholder="Chat with your documents...",
+                    show_label=False,
+                    type="messages"
+                )
+                with gr.Row(elem_classes="input-row"):
+                    message_input = gr.Textbox(
+                        placeholder="Type your question here...",
+                        show_label=False,
+                        container=False,
+                        lines=1,
+                        scale=8
+                    )
+                    submit_button = gr.Button("➤", elem_classes="submit-btn", scale=1)
+                
+                with gr.Accordion("Advanced Settings", open=False):
+                    system_prompt = gr.Textbox(
+                        value="You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem. As a knowledgeable assistant, provide detailed answers using the relevant information from all uploaded documents.",                        
+                        label="System Prompt",
+                        lines=3
+                    )
+                    
+                    with gr.Tab("Generation Parameters"):
+                        temperature = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            step=0.1,
+                            value=0.9,
+                            label="Temperature"
+                        )
+                        max_new_tokens = gr.Slider(
+                            minimum=128,
+                            maximum=8192,
+                            step=64,
+                            value=1024,
+                            label="Max New Tokens",
+                        )
+                        top_p = gr.Slider(
+                            minimum=0.0,
+                            maximum=1.0,
+                            step=0.1,
+                            value=0.95, 
+                            label="Top P"
+                        )
+                        top_k = gr.Slider(
+                            minimum=1,
+                            maximum=100,  
+                            step=1,
+                            value=50,  
+                            label="Top K"
+                        )
+                        penalty = gr.Slider(
+                            minimum=0.0,
+                            maximum=2.0,
+                            step=0.1,
+                            value=1.2,
+                            label="Repetition Penalty"
+                        )
+                        
+                    with gr.Tab("Retrieval Parameters"):
+                        retriever_k = gr.Slider(
+                            minimum=5,
+                            maximum=30,
+                            step=1,
+                            value=15,
+                            label="Initial Retrieval Size (Top K)"
+                        )
+                        merge_threshold = gr.Slider(
+                            minimum=0.1,
+                            maximum=0.9,
+                            step=0.1,
+                            value=0.5,
+                            label="Merge Threshold (lower = more merging)"
+                        )
+
+                submit_button.click(
+                    fn=stream_chat,
+                    inputs=[
+                        message_input, 
+                        chatbot, 
+                        system_prompt, 
+                        temperature, 
+                        max_new_tokens, 
+                        top_p, 
+                        top_k, 
+                        penalty,
+                        retriever_k,
+                        merge_threshold
+                    ],
+                    outputs=chatbot
+                )
+                
+                message_input.submit(
+                    fn=stream_chat,
+                    inputs=[
+                        message_input, 
+                        chatbot, 
+                        system_prompt, 
+                        temperature, 
+                        max_new_tokens, 
+                        top_p, 
+                        top_k, 
+                        penalty,
+                        retriever_k,
+                        merge_threshold
+                    ],
+                    outputs=chatbot
+                )
+    return demo
 
 if __name__ == "__main__":
+    initialize_model_and_tokenizer()
+    demo = create_demo()
     demo.launch()