final.py

# import torch
# from transformers import AutoTokenizer, AutoModelForSequenceClassification
# import networkx as nx
# import spacy
# import pickle
# import pandas as pd
# import google.generativeai as genai
# import json

# # Load spaCy for NER
# nlp = spacy.load("en_core_web_sm")

# # Load the trained ML model
# model_path = "./results/checkpoint-5030"  # Replace with the actual path to your model
# tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
# model = AutoModelForSequenceClassification.from_pretrained(model_path)
# model.eval()

# #########################
# def setup_gemini():
#     genai.configure(api_key='AIzaSyAQzWpSyWyYCM1G5f-G0ulRCQkXuY7admA')
#     model = genai.GenerativeModel('gemini-pro')
#     return model
# #########################

# # Load the knowledge graph
# graph_path = "./models/knowledge_graph.pkl"  # Replace with the actual path to your knowledge graph
# with open(graph_path, 'rb') as f:
#     graph_data = pickle.load(f)

# knowledge_graph = nx.DiGraph()
# knowledge_graph.add_nodes_from(graph_data['nodes'].items())
# for u, edges in graph_data['edges'].items():
#     for v, data in edges.items():
#         knowledge_graph.add_edge(u, v, **data)

# def predict_with_model(text):
#     """Predict whether the news is real or fake using the ML model."""
#     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
#     with torch.no_grad():
#         outputs = model(**inputs)
#     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
#     predicted_label = torch.argmax(probabilities, dim=-1).item()
#     return "FAKE" if predicted_label == 1 else "REAL"

# def update_knowledge_graph(text, is_real):
#     """Update the knowledge graph with the new article."""
#     entities = extract_entities(text)
#     for entity, entity_type in entities:
#         if not knowledge_graph.has_node(entity):
#             knowledge_graph.add_node(
#                 entity,
#                 type=entity_type,
#                 real_count=1 if is_real else 0,
#                 fake_count=0 if is_real else 1
#             )
#         else:
#             if is_real:
#                 knowledge_graph.nodes[entity]['real_count'] += 1
#             else:
#                 knowledge_graph.nodes[entity]['fake_count'] += 1

#     for i, (entity1, _) in enumerate(entities):
#         for entity2, _ in entities[i+1:]:
#             if not knowledge_graph.has_edge(entity1, entity2):
#                 knowledge_graph.add_edge(
#                     entity1,
#                     entity2,
#                     weight=1,
#                     is_real=is_real
#                 )
#             else:
#                 knowledge_graph[entity1][entity2]['weight'] += 1

# def extract_entities(text):
#     """Extract named entities from text using spaCy."""
#     doc = nlp(text)
#     entities = [(ent.text, ent.label_) for ent in doc.ents]
#     return entities

# def predict_with_knowledge_graph(text):
#     """Predict whether the news is real or fake using the knowledge graph."""
#     entities = extract_entities(text)
#     real_score = 0
#     fake_score = 0

#     for entity, _ in entities:
#         if knowledge_graph.has_node(entity):
#             real_count = knowledge_graph.nodes[entity].get('real_count', 0)
#             fake_count = knowledge_graph.nodes[entity].get('fake_count', 0)
#             total = real_count + fake_count
#             if total > 0:
#                 real_score += real_count / total
#                 fake_score += fake_count / total

#     if real_score > fake_score:
#         return "REAL"
#     else:
#         return "FAKE"

# def predict_news(text):
#     """Predict whether the news is real or fake using both the ML model and the knowledge graph."""
#     # Predict with the ML model
#     ml_prediction = predict_with_model(text)
#     is_real = ml_prediction == "REAL"

#     # Update the knowledge graph
#     update_knowledge_graph(text, is_real)

#     # Predict with the knowledge graph
#     kg_prediction = predict_with_knowledge_graph(text)

#     # Combine predictions (for simplicity, we use the ML model's prediction here)
#     # You can enhance this by combining the scores from both predictions
#     return ml_prediction if ml_prediction == kg_prediction else "UNCERTAIN"

# #########################
# # def analyze_content_gemini(model, text):
# #     prompt = f"""Analyze this news text and provide results in the following JSON-like format:

# #     TEXT: {text}

# #     Please provide analysis in these specific sections:

# #     1. GEMINI ANALYSIS:
# #        - Predicted Classification: [Real/Fake]
# #        - Confidence Score: [0-100%]
# #        - Reasoning: [Key points for classification]

# #     2. TEXT CLASSIFICATION:
# #         - Content category/topic
# #         - Writing style: [Formal/Informal/Clickbait]
# #         - Target audience
# #         - Content type: [news/opinion/editorial]

# #     3. SENTIMENT ANALYSIS:
# #        - Primary emotion
# #        - Emotional intensity (1-10)
# #        - Sensationalism Level: [High/Medium/Low]
# #        - Bias Indicators: [List if any]
# #        - Tone: (formal/informal), [Professional/Emotional/Neutral]
# #        - Key emotional triggers

# #     4. ENTITY RECOGNITION:
# #         - Source Credibility: [High/Medium/Low]
# #        - People mentioned
# #        - Organizations
# #        - Locations
# #        - Dates/Time references
# #        - Key numbers/statistics

# #     5. CONTEXT EXTRACTION:
# #        - Main narrative/story
# #        - Supporting elements
# #        - Key claims
# #        - Narrative structure

# #     6. FACT CHECKING:
# #        - Verifiable Claims: [List main claims]
# #        - Evidence Present: [Yes/No]
# #        - Fact Check Score: [0-100%]

# #     Format the response clearly with distinct sections."""
    
# #     response = model.generate_content(prompt)
# #     return response.text

# def analyze_content_gemini(model, text):
#     prompt = f"""Analyze this news text and return a JSON object with the following structure:
#     {{
#         "gemini_analysis": {{
#             "predicted_classification": "Real or Fake",
#             "confidence_score": "0-100",
#             "reasoning": ["point1", "point2"]
#         }},
#         "text_classification": {{
#             "category": "",
#             "writing_style": "Formal/Informal/Clickbait",
#             "target_audience": "",
#             "content_type": "news/opinion/editorial"
#         }},
#         "sentiment_analysis": {{
#             "primary_emotion": "",
#             "emotional_intensity": "1-10",
#             "sensationalism_level": "High/Medium/Low",
#             "bias_indicators": ["bias1", "bias2"],
#             "tone": {{"formality": "formal/informal", "style": "Professional/Emotional/Neutral"}},
#             "emotional_triggers": ["trigger1", "trigger2"]
#         }},
#         "entity_recognition": {{
#             "source_credibility": "High/Medium/Low",
#             "people": ["person1", "person2"],
#             "organizations": ["org1", "org2"],
#             "locations": ["location1", "location2"],
#             "dates": ["date1", "date2"],
#             "statistics": ["stat1", "stat2"]
#         }},
#         "context": {{
#             "main_narrative": "",
#             "supporting_elements": ["element1", "element2"],
#             "key_claims": ["claim1", "claim2"],
#             "narrative_structure": ""
#         }},
#         "fact_checking": {{
#             "verifiable_claims": ["claim1", "claim2"],
#             "evidence_present": "Yes/No",
#             "fact_check_score": "0-100"
#         }}
#     }}

#     Analyze this text and return only the JSON response: {text}"""
    
#     response = model.generate_content(prompt)
#     # return json.loads(response.text)
#     # Add error handling and response cleaning
#     try:
#         # Clean the response text to ensure it's valid JSON
#         cleaned_text = response.text.strip()
#         if cleaned_text.startswith('```json'):
#             cleaned_text = cleaned_text[7:-3]  # Remove ```json and ``` markers
#         return json.loads(cleaned_text)
#     except json.JSONDecodeError:
#         # Return a default structured response if JSON parsing fails
#         return {
#             "gemini_analysis": {
#                 "predicted_classification": "UNCERTAIN",
#                 "confidence_score": "50",
#                 "reasoning": ["Analysis failed to generate valid JSON"]
#             }
#         }


# def clean_gemini_output(text):
#     """Remove markdown formatting from Gemini output"""
#     text = text.replace('##', '')
#     text = text.replace('**', '')
#     return text

# def get_gemini_analysis(text):
#     """Get detailed content analysis from Gemini."""
#     gemini_model = setup_gemini()
#     gemini_analysis = analyze_content_gemini(gemini_model, text)
#     # cleaned_analysis = clean_gemini_output(gemini_analysis)
#     # return cleaned_analysis
#     return gemini_analysis
# #########################

# def main():
#     print("Welcome to the News Classifier!")
#     print("Enter your news text below. Type 'Exit' to quit.")
    
#     while True:
#         news_text = input("\nEnter news text: ")
        
#         if news_text.lower() == 'exit':
#             print("Thank you for using the News Classifier!")
#             return
            
#         # First get ML and Knowledge Graph prediction
#         prediction = predict_news(news_text)
#         print(f"\nML and Knowledge Graph Analysis: {prediction}")
        
#         # Then get Gemini analysis
#         print("\n=== Detailed Gemini Analysis ===")
#         gemini_result = get_gemini_analysis(news_text)
#         print(gemini_result)


# if __name__ == "__main__":
#     main()

import streamlit as st
# Page config
st.set_page_config(
    page_title="Nexus NLP News Classifier",
    page_icon="📰",
    layout="wide"
)
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, DebertaV2Tokenizer
import networkx as nx
import spacy
import pickle
import google.generativeai as genai
import json
import os
import dotenv


# Load environment variables
dotenv.load_dotenv()

# Load models and resources
@st.cache_resource
def load_nlp():
    return spacy.load("en_core_web_sm")

@st.cache_resource
def load_model():
    model_path = "./results/checkpoint-753"
    tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-small')
    model = AutoModelForSequenceClassification.from_pretrained(model_path)
    model.eval()
    return tokenizer, model

@st.cache_resource
def load_knowledge_graph():
    graph_path = "./knowledge_graph_final.pkl"
    with open(graph_path, 'rb') as f:
        graph_data = pickle.load(f)
    knowledge_graph = nx.DiGraph()
    knowledge_graph.add_nodes_from(graph_data['nodes'].items())
    for u, edges in graph_data['edges'].items():
        for v, data in edges.items():
            knowledge_graph.add_edge(u, v, **data)
    return knowledge_graph

def setup_gemini():
    genai.configure(api_key=os.getenv("GEMINI_API"))
    model = genai.GenerativeModel('gemini-pro')
    return model

# Initialize resources
nlp = load_nlp()
tokenizer, model = load_model()
knowledge_graph = load_knowledge_graph()

def predict_with_model(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_label = torch.argmax(probabilities, dim=-1).item()
    confidence = probabilities[0][predicted_label].item() * 100
    return "FAKE" if predicted_label == 1 else "REAL", confidence

def extract_entities(text):
    doc = nlp(text)
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    return entities

def update_knowledge_graph(text, is_real):
    entities = extract_entities(text)
    for entity, entity_type in entities:
        if not knowledge_graph.has_node(entity):
            knowledge_graph.add_node(
                entity,
                type=entity_type,
                real_count=1 if is_real else 0,
                fake_count=0 if is_real else 1
            )
        else:
            if is_real:
                knowledge_graph.nodes[entity]['real_count'] += 1
            else:
                knowledge_graph.nodes[entity]['fake_count'] += 1

    for i, (entity1, _) in enumerate(entities):
        for entity2, _ in entities[i+1:]:
            if not knowledge_graph.has_edge(entity1, entity2):
                knowledge_graph.add_edge(
                    entity1,
                    entity2,
                    weight=1,
                    is_real=is_real
                )
            else:
                knowledge_graph[entity1][entity2]['weight'] += 1

def predict_with_knowledge_graph(text):
    entities = extract_entities(text)
    real_score = 0
    fake_score = 0

    for entity, _ in entities:
        if knowledge_graph.has_node(entity):
            real_count = knowledge_graph.nodes[entity].get('real_count', 0)
            fake_count = knowledge_graph.nodes[entity].get('fake_count', 0)
            total = real_count + fake_count
            if total > 0:
                real_score += real_count / total
                fake_score += fake_count / total

    total_score = real_score + fake_score
    if total_score == 0:
        return "UNCERTAIN", 50.0
    
    if real_score > fake_score:
        confidence = (real_score / total_score) * 100
        return "REAL", confidence
    else:
        confidence = (fake_score / total_score) * 100
        return "FAKE", confidence

def analyze_content_gemini(model, text):
    prompt = f"""Analyze this news text and return a JSON object with the following structure:
    {{
        "gemini_analysis": {{
            "predicted_classification": "Real or Fake",
            "confidence_score": "0-100",
            "reasoning": ["point1", "point2"]
        }},
        "text_classification": {{
            "category": "",
            "writing_style": "Formal/Informal/Clickbait",
            "target_audience": "",
            "content_type": "news/opinion/editorial"
        }},
        "sentiment_analysis": {{
            "primary_emotion": "",
            "emotional_intensity": "1-10",
            "sensationalism_level": "High/Medium/Low",
            "bias_indicators": ["bias1", "bias2"],
            "tone": {{"formality": "formal/informal", "style": "Professional/Emotional/Neutral"}},
            "emotional_triggers": ["trigger1", "trigger2"]
        }},
        "entity_recognition": {{
            "source_credibility": "High/Medium/Low",
            "people": ["person1", "person2"],
            "organizations": ["org1", "org2"],
            "locations": ["location1", "location2"],
            "dates": ["date1", "date2"],
            "statistics": ["stat1", "stat2"]
        }},
        "context": {{
            "main_narrative": "",
            "supporting_elements": ["element1", "element2"],
            "key_claims": ["claim1", "claim2"],
            "narrative_structure": ""
        }},
        "fact_checking": {{
            "verifiable_claims": ["claim1", "claim2"],
            "evidence_present": "Yes/No",
            "fact_check_score": "0-100"
        }}
    }}

    Analyze this text and return only the JSON response: {text}"""
    
    response = model.generate_content(prompt)
    try:
        cleaned_text = response.text.strip()
        if cleaned_text.startswith('```json'):
            cleaned_text = cleaned_text[7:-3]
        return json.loads(cleaned_text)
    except json.JSONDecodeError:
        return {
            "gemini_analysis": {
                "predicted_classification": "UNCERTAIN",
                "confidence_score": "50",
                "reasoning": ["Analysis failed to generate valid JSON"]
            }
        }

def main():
    st.title("📰 Nexus NLP News Classifier")
    st.write("Enter news text below to analyze its authenticity")

    # Query parameters for API functionality
    query_params = st.query_params
    if "text" in query_params:
        text_input = query_params["text"][0]
        ml_prediction, ml_confidence = predict_with_model(text_input)
        st.json({"prediction": ml_prediction, "confidence": ml_confidence})
        return

    # Regular UI
    news_text = st.text_area("News Text", height=200)

    if st.button("Analyze"):
        if news_text:
            with st.spinner("Analyzing..."):
                # Get all predictions
                ml_prediction, ml_confidence = predict_with_model(news_text)
                kg_prediction, kg_confidence = predict_with_knowledge_graph(news_text)
                update_knowledge_graph(news_text, ml_prediction == "REAL")
                
                gemini_model = setup_gemini()
                gemini_result = analyze_content_gemini(gemini_model, news_text)

                # Display results
                col1, col2, col3 = st.columns(3)
                
                with col1:
                    st.subheader("ML Model Analysis")
                    st.metric("Prediction", ml_prediction)
                    st.metric("Confidence", f"{ml_confidence:.2f}%")

                with col2:
                    st.subheader("Knowledge Graph Analysis")
                    st.metric("Prediction", kg_prediction)
                    st.metric("Confidence", f"{kg_confidence:.2f}%")

                with col3:
                    st.subheader("Gemini Analysis")
                    gemini_pred = gemini_result["gemini_analysis"]["predicted_classification"]
                    gemini_conf = gemini_result["gemini_analysis"]["confidence_score"]
                    st.metric("Prediction", gemini_pred)
                    st.metric("Confidence", f"{gemini_conf}%")

                with st.expander("View Detailed Analysis"):
                    st.json(gemini_result)

                with st.expander("Named Entities"):
                    entities = extract_entities(news_text)
                    st.write(entities)

        else:
            st.warning("Please enter some text to analyze")

if __name__ == "__main__":
    main()