Update app.py

app.py

@@ -682,158 +682,63 @@ def upload_dataset():
     return False
 
 def run_analysis():
-    """Run the AI analysis with beautiful progress indicators"""
+    st.write("🔍 DEBUG: Starting analysis...")
+    
     if st.session_state.dataset is None:
         st.warning("Please upload a dataset first.")
         return
     
-    # Check for API key from environment or session state
+    st.write(f"🔍 DEBUG: Dataset shape: {st.session_state.dataset.shape}")
+    
+    # Test API key
     api_key = os.environ.get('GROQ_API_KEY') or st.session_state.get('groq_api_key')
     if not api_key:
-        st.warning("Please set GROQ_API_KEY environment variable or enter it in the sidebar.")
+        st.error("No API key found")
         return
     
-    # Create agent
-    with st.spinner("🤖 Initializing AI agent..."):
-        agent = create_agent()
-        if agent is None:
-            st.error("Failed to initialize AI agent. Check your API key.")
-            return
-    
-    st.session_state.agent = agent
+    st.write("🔍 DEBUG: API key found")
     
-    # Save dataset temporarily
-    temp_file = "temp_dataset.csv"
-    st.session_state.dataset.to_csv(temp_file, index=False)
-    
-    # Beautiful progress tracking
-    progress_container = st.container()
+    # Test agent creation
+    try:
+        st.write("🔍 DEBUG: Creating agent...")
+        agent = create_agent()
+        st.write(f"🔍 DEBUG: Agent created: {agent is not None}")
+    except Exception as e:
+        st.error(f"❌ Agent creation failed: {e}")
+        return
     
-    with progress_container:
-        st.markdown("### 🚀 AI Analysis in Progress")
-        
-        # Progress bar
-        progress_bar = st.progress(0)
-        status_text = st.empty()
-        
-        # Step indicators
-        steps = [
-            ("🔍", "Analyzing dataset structure"),
-            ("📊", "Examining columns and data quality"),
-            ("🧠", "Generating AI insights"),
-            ("📈", "Planning visualizations"),
-            ("🎨", "Creating charts"),
-            ("🎯", "Formulating recommendations")
-        ]
-        
-        step_cols = st.columns(len(steps))
-        step_indicators = []
-        
-        for i, (icon, desc) in enumerate(steps):
-            with step_cols[i]:
-                step_indicators.append(st.empty())
-                step_indicators[i].markdown(f"""
-                <div style="text-align: center; padding: 1rem; opacity: 0.3;">
-                    <div style="font-size: 2rem;">{icon}</div>
-                    <div style="font-size: 0.8rem; margin-top: 0.5rem;">{desc}</div>
-                </div>
-                """, unsafe_allow_html=True)
+    # Test dataset save
+    try:
+        temp_file = "temp_dataset.csv"
+        st.session_state.dataset.to_csv(temp_file, index=False)
+        st.write("🔍 DEBUG: Dataset saved successfully")
+    except Exception as e:
+        st.error(f"❌ Dataset save failed: {e}")
+        return
     
+    # Test analysis
     try:
-        # Step 1
-        step_indicators[0].markdown(f"""
-        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
-            <div style="font-size: 2rem;">🔍</div>
-            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Analyzing Structure</div>
-        </div>
-        """, unsafe_allow_html=True)
-        status_text.markdown("**🔍 AI agent analyzing dataset structure...**")
-        progress_bar.progress(15)
-        time.sleep(1)
-        
-        # Step 2
-        step_indicators[1].markdown(f"""
-        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
-            <div style="font-size: 2rem;">📊</div>
-            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Examining Data</div>
-        </div>
-        """, unsafe_allow_html=True)
-        status_text.markdown("**📊 Analyzing columns and data quality...**")
-        progress_bar.progress(30)
-        time.sleep(1)
-        
-        # Step 3
-        step_indicators[2].markdown(f"""
-        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
-            <div style="font-size: 2rem;">🧠</div>
-            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">AI Thinking</div>
-        </div>
-        """, unsafe_allow_html=True)
-        status_text.markdown("**🧠 Generating insights with AI...**")
-        progress_bar.progress(50)
-        time.sleep(1)
-        
-        # Step 4
-        step_indicators[3].markdown(f"""
-        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
-            <div style="font-size: 2rem;">📈</div>
-            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Planning Charts</div>
-        </div>
-        """, unsafe_allow_html=True)
-        status_text.markdown("**📈 Planning optimal visualizations...**")
-        progress_bar.progress(70)
-        time.sleep(1)
-        
-        # Step 5
-        step_indicators[4].markdown(f"""
-        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
-            <div style="font-size: 2rem;">🎨</div>
-            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Creating Charts</div>
-        </div>
-        """, unsafe_allow_html=True)
-        status_text.markdown("**🎨 Creating beautiful visualizations...**")
-        progress_bar.progress(85)
-        
-        # Run the actual analysis
+        st.write("🔍 DEBUG: Starting agent analysis...")
         results = agent.analyze_dataset(temp_file)
-        
-        # Step 6
-        step_indicators[5].markdown(f"""
-        <div style="text-align: center; padding: 1rem; opacity: 1; background: linear-gradient(135deg, #eff6ff 0%, #dbeafe 100%); border-radius: 12px;">
-            <div style="font-size: 2rem;">🎯</div>
-            <div style="font-size: 0.8rem; margin-top: 0.5rem; font-weight: 600;">Final Recommendations</div>
-        </div>
-        """, unsafe_allow_html=True)
-        status_text.markdown("**🎯 Formulating actionable recommendations...**")
-        progress_bar.progress(100)
-        
-        # Clean up temp file
-        if os.path.exists(temp_file):
-            os.remove(temp_file)
+        st.write(f"🔍 DEBUG: Analysis complete: {type(results)}")
         
         if "error" in results:
-            st.error(f"❌ Analysis failed: {results['error']}")
-            return
-        
-        st.session_state.analysis_results = results
-        st.session_state.analysis_complete = True
-        
-        # Success animation
-        status_text.markdown("**✅ Analysis completed successfully!**")
-        
-        # Show completion message
-        st.balloons()
-        time.sleep(1)
-        
-        # Clear progress and show results
-        progress_container.empty()
-        st.rerun()
-        
+            st.error(f"Analysis error: {results['error']}")
+        else:
+            st.success("✅ Analysis successful!")
+            st.session_state.analysis_results = results
+            st.session_state.analysis_complete = True
+            st.rerun()
+            
     except Exception as e:
-        st.error(f"❌ Analysis failed: {str(e)}")
+        st.error(f"❌ Analysis failed: {e}")
+        import traceback
+        st.code(traceback.format_exc())
+    
+    finally:
         if os.path.exists(temp_file):
             os.remove(temp_file)
-
+            
 def display_results():
     """Display beautiful analysis results"""
     results = st.session_state.analysis_results