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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 | |
| ) |