try2

app.py

@@ -165,44 +165,63 @@ def main():
 
                 # Detailed analysis sections
                 with st.expander("View Detailed Analysis"):
-                    try:
-                        # Text Classification
-                        st.subheader("📝 Text Classification")
-                        text_class = gemini_result.get('text_classification', {})
-                        st.write(f"Category: {text_class.get('category', 'N/A')}")
-                        st.write(f"Writing Style: {text_class.get('writing_style', 'N/A')}")
-                        st.write(f"Target Audience: {text_class.get('target_audience', 'N/A')}")
-                        st.write(f"Content Type: {text_class.get('content_type', 'N/A')}")
-                        
-                        # Sentiment Analysis
-                        st.subheader("🎭 Sentiment Analysis")
-                        sentiment = gemini_result.get('sentiment_analysis', {})
-                        st.write(f"Primary Emotion: {sentiment.get('primary_emotion', 'N/A')}")
-                        st.write(f"Emotional Intensity: {sentiment.get('emotional_intensity', 'N/A')}/10")
-                        st.write(f"Sensationalism Level: {sentiment.get('sensationalism_level', 'N/A')}")
-                        
-                        # Entity Recognition
-                        st.subheader("🔍 Entity Recognition")
-                        entities = gemini_result.get('entity_recognition', {})
-                        st.write(f"Source Credibility: {entities.get('source_credibility', 'N/A')}")
-                        st.write("Key People:", ", ".join(entities.get('people', ['N/A'])))
-                        st.write("Organizations:", ", ".join(entities.get('organizations', ['N/A'])))
-                        
-                        # Context & Claims
-                        st.subheader("📊 Context & Claims")
-                        context = gemini_result.get('context', {})
-                        st.write("Main Narrative:", context.get('main_narrative', 'N/A'))
-                        st.write("Key Claims:")
-                        for claim in gemini_result.get('fact_checking', {}).get('verifiable_claims', ['N/A']):
-                            st.write(f"• {claim}")
-                        
-                        # Reasoning
-                        st.subheader("💭 Analysis Reasoning")
-                        for point in gemini_result.get('gemini_analysis', {}).get('reasoning', ['N/A']):
-                            st.write(f"• {point}")
+                    with st.expander("View Detailed Analysis"):
+                        try:
+                            # Text Classification
+                            st.subheader("📝 Text Classification")
+                            text_class = gemini_result.get('text_classification', {})
+                            st.write(f"Category: {text_class.get('category', 'N/A')}")
+                            st.write(f"Writing Style: {text_class.get('writing_style', 'N/A')}")
+                            st.write(f"Target Audience: {text_class.get('target_audience', 'N/A')}")
+                            st.write(f"Content Type: {text_class.get('content_type', 'N/A')}")
                             
-                    except Exception as e:
-                        st.error("Error processing Gemini analysis results")
+                            # Sentiment Analysis
+                            st.subheader("🎭 Sentiment Analysis")
+                            sentiment = gemini_result.get('sentiment_analysis', {})
+                            st.write(f"Primary Emotion: {sentiment.get('primary_emotion', 'N/A')}")
+                            st.write(f"Emotional Intensity: {sentiment.get('emotional_intensity', 'N/A')}/10")
+                            st.write(f"Sensationalism Level: {sentiment.get('sensationalism_level', 'N/A')}")
+                            st.write("Bias Indicators:", ", ".join(sentiment.get('bias_indicators', ['N/A'])))
+                            
+                            tone = sentiment.get('tone', {})
+                            st.write(f"Tone Formality: {tone.get('formality', 'N/A')}")
+                            st.write(f"Tone Style: {tone.get('style', 'N/A')}")
+                            st.write("Emotional Triggers:", ", ".join(sentiment.get('emotional_triggers', ['N/A'])))
+                            
+                            # Entity Recognition
+                            st.subheader("🔍 Entity Recognition")
+                            entities = gemini_result.get('entity_recognition', {})
+                            st.write(f"Source Credibility: {entities.get('source_credibility', 'N/A')}")
+                            st.write("People:", ", ".join(entities.get('people', ['N/A'])))
+                            st.write("Organizations:", ", ".join(entities.get('organizations', ['N/A'])))
+                            st.write("Locations:", ", ".join(entities.get('locations', ['N/A'])))
+                            st.write("Dates:", ", ".join(entities.get('dates', ['N/A'])))
+                            st.write("Statistics:", ", ".join(entities.get('statistics', ['N/A'])))
+                            
+                            # Context
+                            st.subheader("📰 Context")
+                            context = gemini_result.get('context', {})
+                            st.write("Main Narrative:", context.get('main_narrative', 'N/A'))
+                            st.write("Supporting Elements:", ", ".join(context.get('supporting_elements', ['N/A'])))
+                            st.write("Key Claims:", ", ".join(context.get('key_claims', ['N/A'])))
+                            st.write("Narrative Structure:", context.get('narrative_structure', 'N/A'))
+                            
+                            # Fact Checking
+                            st.subheader("✔️ Fact Checking")
+                            fact_check = gemini_result.get('fact_checking', {})
+                            st.write("Verifiable Claims:")
+                            for claim in fact_check.get('verifiable_claims', ['N/A']):
+                                st.write(f"• {claim}")
+                            st.write(f"Evidence Present: {fact_check.get('evidence_present', 'N/A')}")
+                            st.write(f"Fact Check Score: {fact_check.get('fact_check_score', 'N/A')}/100")
+                            
+                            # Analysis Reasoning
+                            st.subheader("💭 Analysis Reasoning")
+                            for point in gemini_result.get('gemini_analysis', {}).get('reasoning', ['N/A']):
+                                st.write(f"• {point}")
+                                
+                        except Exception as e:
+                            st.error("Error processing Gemini analysis results")
 
 
                 with st.expander("Named Entities"):

final.py

@@ -146,7 +146,7 @@ def predict_with_knowledge_graph(text, knowledge_graph, nlp):
 
 def analyze_content_gemini(model, text):
     """Analyze content using Gemini model"""
-    prompt = f"""Analyze this news text and return a JSON object with the following structure:
+    prompt = f"""Analyze this news text and return a JSON object with the following exact structure:
     {{
         "gemini_analysis": {{
             "predicted_classification": "Real or Fake",