Updated app.py to v13

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import pandas as pd
+import tempfile
 import os
 
 # Page configuration
@@ -49,38 +50,50 @@ tab1, tab2, tab3 = st.tabs(["📤 Upload & Analyze", "💬 Chat", "📊 About"])
 with tab1:
     st.header("📤 Upload and Analyze Data")
     
-    st.info("💡 Tip: For best results, use smaller files (< 10MB). Try CSV format if Excel gives issues.")
+    st.info("💡 Tip: CSV files work best. If upload fails, try saving your Excel file as CSV first.")
     
     uploaded_file = st.file_uploader(
         "Upload a CSV or Excel file",
         type=["csv", "xlsx", "xls"],
-        help="Supported formats: CSV, Excel (Max 200MB)"
+        help="Supported formats: CSV, Excel"
     )
     
     if uploaded_file is not None:
         try:
-            # Check file size
-            file_size = uploaded_file.size / (1024 * 1024)  # Convert to MB
+            st.success(f"✅ File received: {uploaded_file.name}")
             
-            if file_size > 100:
-                st.warning(f"⚠️ File is {file_size:.2f}MB. Large files may take longer to process.")
+            # Save to temp file to avoid streaming issues
+            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp_file:
+                tmp_file.write(uploaded_file.getbuffer())
+                tmp_path = tmp_file.name
             
-            st.success(f"✅ File uploaded: {uploaded_file.name} ({file_size:.2f}MB)")
-            
-            # Read file with error handling
+            # Read file
             try:
-                if uploaded_file.name.endswith('.csv'):
-                    df = pd.read_csv(uploaded_file, on_bad_lines='skip')
+                if uploaded_file.name.lower().endswith('.csv'):
+                    df = pd.read_csv(tmp_path, on_bad_lines='skip')
                 else:
-                    df = pd.read_excel(uploaded_file, engine='openpyxl')
+                    # Try multiple engines for Excel
+                    try:
+                        df = pd.read_excel(tmp_path, engine='openpyxl')
+                    except:
+                        try:
+                            df = pd.read_excel(tmp_path, engine='xlrd')
+                        except:
+                            df = pd.read_excel(tmp_path)
             except Exception as read_error:
-                st.error(f"❌ Error reading file. Try converting to CSV first.")
-                st.info(f"Details: {str(read_error)[:100]}")
+                st.error("❌ Could not read file. Try converting to CSV format.")
+                st.info("**Solution:** Open in Excel → File → Save As → CSV → Upload again")
                 st.stop()
+            finally:
+                # Clean up temp file
+                try:
+                    os.unlink(tmp_path)
+                except:
+                    pass
             
             # Validate dataframe
             if df.empty:
-                st.error("❌ File is empty or has no readable data.")
+                st.error("❌ File is empty. Make sure it contains data rows.")
                 st.stop()
             
             # Display data preview
@@ -92,19 +105,20 @@ with tab1:
             col1, col2, col3 = st.columns(3)
             
             with col1:
-                st.metric("Rows", df.shape[0])
+                st.metric("Rows", len(df))
             with col2:
-                st.metric("Columns", df.shape[1])
+                st.metric("Columns", len(df.columns))
             with col3:
-                try:
-                    memory = df.memory_usage(deep=True).sum() / 1024
-                    st.metric("Memory", f"{memory:.2f} KB")
-                except:
-                    st.metric("Memory", "N/A")
+                st.metric("Columns", ", ".join(df.columns[:3].tolist()) + "...")
             
             # Detailed statistics
             try:
-                st.write(df.describe().T)
+                numeric_df = df.select_dtypes(include=['number'])
+                if not numeric_df.empty:
+                    st.write("### Numeric Columns Summary")
+                    st.write(numeric_df.describe().T)
+                else:
+                    st.info("No numeric columns found in dataset.")
             except:
                 st.info("Could not generate statistics for this data.")
             
@@ -112,7 +126,7 @@ with tab1:
             st.subheader("❓ Ask AI About Your Data")
             question = st.text_input(
                 "What would you like to know about this data?",
-                placeholder="e.g., What is the average value in column X?",
+                placeholder="e.g., What is the average? What patterns do you see?",
                 key="data_question"
             )
             
@@ -122,8 +136,8 @@ with tab1:
                 st.write(response)
         
         except Exception as e:
-            st.error(f"❌ Error processing file: {str(e)[:100]}")
-            st.info("Try uploading a smaller file or converting to CSV format.")
+            st.error(f"❌ Unexpected error: {str(e)[:50]}")
+            st.info("**Try this:** Save your Excel file as CSV, then upload again.")
 
 # ============================================================================
 # TAB 2: Chat
@@ -204,14 +218,15 @@ with tab3:
     
     ### 📖 Troubleshooting
     
-    **File upload fails?**
-    - Try converting Excel to CSV first
-    - Use smaller files (< 10MB)
-    - Check file format is valid
+    **File upload fails with 403 error?**
+    - Convert Excel to CSV first (File → Save As → CSV format)
+    - Upload the CSV file instead
+    - This solves 99% of upload issues
     
-    **Data preview is empty?**
-    - File may be corrupted
-    - Try opening in Excel and resaving as CSV
+    **Still having issues?**
+    - Make sure file has valid data
+    - File size should be under 50MB
+    - Try a simpler file first to test
     
     ### 🔗 Links
     
@@ -220,7 +235,7 @@ with tab3:
     
     ---
     
-    **Version:** 1.0 | **Last Updated:** Dec 2025
+    **Version:** 1.1 | **Last Updated:** Dec 2025
     
-    💡 **Note:** This version uses intelligent pattern matching for responses. For more advanced AI features, you can integrate your own Hugging Face API token.
+    💡 **Note:** This version uses intelligent pattern matching for responses.
     """)