Update app.py

app.py

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+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('Agg')  # Use non-interactive backend
+import os
+import tempfile
+import base64
+from io import BytesIO
+from pandasai import SmartDataframe
+from langchain_groq.chat_models import ChatGroq
+
+# HARDCODED API KEY - REPLACE WITH YOUR ACTUAL KEY
+API_KEY = "gsk_YOUR_ACTUAL_API_KEY_HERE"  # Replace with your real API key
+
+# Global variables to store data
+current_df = None
+llm = None
+
+def initialize_llm():
+    """Initialize the Groq LLM"""
+    global llm
+    try:
+        if API_KEY == "gsk_YOUR_ACTUAL_API_KEY_HERE":
+            return "❌ Please replace 'gsk_YOUR_ACTUAL_API_KEY_HERE' with your actual Groq API key", None
+        
+        llm = ChatGroq(
+            model_name="mixtral-8x7b-32768",
+            api_key=API_KEY,
+            temperature=0
+        )
+        return "✅ Groq LLM initialized successfully", llm
+    except Exception as e:
+        return f"❌ Failed to initialize Groq LLM: {str(e)}", None
+
+def process_csv(file):
+    """Process uploaded CSV file"""
+    global current_df
+    
+    if file is None:
+        return "No file uploaded", None, None
+    
+    try:
+        # Read the CSV file
+        current_df = pd.read_csv(file.name)
+        
+        # Create preview
+        preview = current_df.head().to_html(classes='table table-striped', table_id='data-preview')
+        
+        # Create info
+        info = f"""
+        **File Info:**
+        - Shape: {current_df.shape[0]} rows × {current_df.shape[1]} columns
+        - Columns: {', '.join(current_df.columns.tolist())}
+        """
+        
+        return "✅ CSV file loaded successfully", preview, info
+        
+    except Exception as e:
+        return f"❌ Error reading CSV: {str(e)}", None, None
+
+def chat_with_data(query):
+    """Process user query and return response"""
+    global current_df, llm
+    
+    if current_df is None:
+        return "❌ Please upload a CSV file first", None
+    
+    if llm is None:
+        status, _ = initialize_llm()
+        if llm is None:
+            return status, None
+    
+    if not query.strip():
+        return "❌ Please enter a query", None
+    
+    try:
+        # Create temporary directory for charts
+        temp_dir = tempfile.mkdtemp()
+        
+        # Create SmartDataframe
+        sdf = SmartDataframe(
+            current_df, 
+            config={
+                "llm": llm,
+                "verbose": True,
+                "save_charts": True,
+                "save_charts_path": temp_dir,
+                "custom_whitelisted_dependencies": ["matplotlib", "seaborn", "plotly"]
+            }
+        )
+        
+        # Process the query
+        result = sdf.chat(query)
+        
+        # Handle different types of results
+        if isinstance(result, str):
+            # Text response
+            return f"📢 **Response:**\n{result}", None
+            
+        elif hasattr(result, 'savefig'):
+            # Matplotlib figure
+            try:
+                # Save figure to bytes
+                img_buffer = BytesIO()
+                result.savefig(img_buffer, format='png', dpi=150, bbox_inches='tight')
+                img_buffer.seek(0)
+                
+                # Save to temporary file for Gradio
+                temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
+                with open(temp_file.name, 'wb') as f:
+                    f.write(img_buffer.getvalue())
+                
+                plt.close(result)  # Close the figure to free memory
+                
+                return "📈 **Chart Generated:**", temp_file.name
+                
+            except Exception as chart_error:
+                return f"❌ Error saving chart: {str(chart_error)}", None
+                
+        elif isinstance(result, pd.DataFrame):
+            # DataFrame result
+            html_table = result.to_html(classes='table table-striped', max_rows=100)
+            return f"📊 **Data Result:**\n{html_table}", None
+            
+        else:
+            # Other types of results
+            return f"📊 **Result:**\n{str(result)}", None
+            
+    except Exception as e:
+        error_msg = f"❌ Error: {str(e)}"
+        
+        # Provide specific error guidance
+        if "403" in str(e):
+            error_msg += "\n\n🔐 **403 Forbidden Error** - This usually means:\n"
+            error_msg += "- Invalid API key\n"
+            error_msg += "- API key doesn't have permission for this model\n"
+            error_msg += "- Rate limit exceeded\n"
+            error_msg += "- Model name is incorrect"
+        elif "rate limit" in str(e).lower():
+            error_msg += "\n\n⏰ **Rate Limit** - Please wait a moment before trying again"
+        elif "timeout" in str(e).lower():
+            error_msg += "\n\n⏱️ **Timeout** - The query took too long. Try a simpler request"
+            
+        return error_msg, None
+
+def get_debug_info():
+    """Get debug information"""
+    if API_KEY and API_KEY != "gsk_YOUR_ACTUAL_API_KEY_HERE":
+        return f"✅ API Key loaded successfully\nKey starts with: {API_KEY[:10]}..."
+    else:
+        return "❌ Replace 'gsk_YOUR_ACTUAL_API_KEY_HERE' with your actual API key"
+
+# Initialize LLM on startup
+init_status, _ = initialize_llm()
+
+# Create Gradio interface
+with gr.Blocks(title="📊 CSV Chat with Groq + PandasAI", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 📊 Chat with Your CSV using PandasAI + Groq")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # File upload section
+            gr.Markdown("## 📁 Upload CSV File")
+            file_input = gr.File(
+                label="Upload your CSV file",
+                file_types=[".csv"],
+                type="filepath"
+            )
+            
+            upload_status = gr.Textbox(
+                label="Upload Status",
+                interactive=False,
+                value=init_status
+            )
+            
+            # Data preview section
+            gr.Markdown("## 📋 Data Preview")
+            data_preview = gr.HTML(label="Data Preview")
+            data_info = gr.Markdown()
+            
+        with gr.Column(scale=1):
+            # Debug and help section
+            gr.Markdown("## 🔧 Debug Info")
+            debug_btn = gr.Button("Show Debug Info")
+            debug_info = gr.Textbox(label="Debug Information", interactive=False)
+            
+            gr.Markdown("## 📝 Example Queries")
+            gr.Markdown("""
+            - "Show me the first 10 rows"
+            - "What are the column names?"
+            - "Create a histogram of [column_name]"
+            - "Show me the summary statistics"
+            - "Plot the top 5 values in [column_name]"
+            - "Create a bar chart showing [column1] vs [column2]"
+            """)
+    
+    # Chat section
+    gr.Markdown("## 💬 Chat with Your Data")
+    
+    with gr.Row():
+        query_input = gr.Textbox(
+            label="Ask a question or request a chart",
+            placeholder="What would you like to know about your data?",
+            lines=3,
+            scale=4
+        )
+        submit_btn = gr.Button("Submit Query", variant="primary", scale=1)
+    
+    # Results section
+    with gr.Row():
+        with gr.Column():
+            response_output = gr.Markdown(label="Response")
+        with gr.Column():
+            chart_output = gr.Image(label="Generated Chart", type="filepath")
+    
+    # Event handlers
+    file_input.change(
+        fn=process_csv,
+        inputs=[file_input],
+        outputs=[upload_status, data_preview, data_info]
+    )
+    
+    debug_btn.click(
+        fn=get_debug_info,
+        outputs=[debug_info]
+    )
+    
+    submit_btn.click(
+        fn=chat_with_data,
+        inputs=[query_input],
+        outputs=[response_output, chart_output]
+    )
+    
+    query_input.submit(
+        fn=chat_with_data,
+        inputs=[query_input],
+        outputs=[response_output, chart_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        share=False,  # Set to True if you want a public link
+        debug=True,
+        show_error=True
+    )