Fixed return NULL issue from gemini

app.py

@@ -44,7 +44,7 @@ def generate_knowledge_graph_viz(text, nlp, tokenizer, model):
 
     # Randomly select subset of edges (e.g. 60% of edges)
     edges = list(kg_builder.knowledge_graph.edges())
-    selected_edges = random.sample(edges, k=int(len(edges) * 0.6))
+    selected_edges = random.sample(edges, k=int(len(edges) * 0.3))
     
     # Create a new graph with selected edges
     selected_graph = nx.DiGraph()
@@ -140,14 +140,37 @@ def main():
                 update_knowledge_graph(news_text, ml_prediction == "REAL", knowledge_graph, nlp)
                 
                 # Get Gemini analysis
-                gemini_model = setup_gemini()
-                gemini_result = analyze_content_gemini(gemini_model, news_text)
+                # Get Gemini analysis with retries
+                max_retries = 3
+                retry_count = 0
+                gemini_result = None
 
-                # First show the high-level metrics in columns
-                # In the main() function:
+                while retry_count < max_retries:
+                    try:
+                        gemini_model = setup_gemini()
+                        gemini_result = analyze_content_gemini(gemini_model, news_text)
+                        
+                        # Check if we got valid results
+                        if gemini_result and gemini_result.get('gemini_analysis'):
+                            break
+                            
+                    except Exception:
+                        pass
+                        
+                    retry_count += 1
+                    
+                # Use default values if all retries failed
+                if not gemini_result:
+                    gemini_result = {
+                        "gemini_analysis": {
+                            "predicted_classification": "UNCERTAIN",
+                            "confidence_score": "50",
+                            "reasoning": ["Analysis temporarily unavailable"]
+                        }
+                    }
 
                 # Display metrics in columns
-                col1, col2 = st.columns(2)
+                col1 = st.columns(1)
 
                 with col1:
                     st.subheader("ML Model and Knowedge Graph Analysis")
@@ -159,12 +182,12 @@ def main():
                 #     st.metric("Prediction", kg_prediction)
                 #     st.metric("Confidence", f"{kg_confidence:.2f}%")
 
-                with col2:
-                    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 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}%")
 
                 # Single expander for all analysis details
                 with st.expander("Detailed Analysis"):