Update app.py

app.py

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+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from huggingface_hub import InferenceClient
+import os
+
+# Initialize HF Inference client
+client = InferenceClient(token=os.environ.get("HF_TOKEN"))
+MAX_TOKENS = 1000
+
+def analyze_data(query, df, chat_history):
+    """Process user query using HF API and generate response"""
+    if df is None:
+        return chat_history + [[query, "Please upload a CSV file first!"]]
+    
+    try:
+        # Generate code using StarCoder model
+        code_response = client.text_generation(
+            prompt=f"""
+            Generate Python code to answer this data analysis question:
+            Dataset columns: {list(df.columns)}
+            Question: {query}
+            
+            Return only the code with these requirements:
+            1. Use pandas and matplotlib
+            2. Store result in 'result' variable
+            3. Create plot using plt.figure()
+            4. No user input or file operations
+            """,
+            model="bigcode/starcoder",
+            max_new_tokens=MAX_TOKENS,
+            temperature=0.2
+        )
+        
+        # Clean and execute code
+        code = clean_code(code_response)
+        exec_env = {'df': df, 'plt': plt}
+        exec(code, exec_env)
+        
+        # Prepare response
+        response = []
+        if 'result' in exec_env:
+            response.append(f"Analysis Result:\n{str(exec_env['result']}")
+            
+        fig = exec_env.get('plt', plt).gcf()
+        if fig.get_axes():
+            fig.savefig("temp_plot.png")
+            response.append(gr.Image("temp_plot.png"))
+        
+        return chat_history + [[query, "\n\n".join(response)]]
+    
+    except Exception as e:
+        return chat_history + [[query, f"Error: {str(e)}"]]
+
+def clean_code(code):
+    """Sanitize generated code"""
+    # Remove dangerous patterns
+    forbidden_patterns = [
+        'os.', 'subprocess', 'sys.', 'shutil', 'open(',
+        '__', 'lambda', 'exec(', 'eval(', 'getattr'
+    ]
+    
+    for pattern in forbidden_patterns:
+        if pattern in code:
+            raise ValueError(f"Forbidden pattern detected: {pattern}")
+            
+    return code
+
+def handle_file(file):
+    """Process uploaded CSV file"""
+    try:
+        return pd.read_csv(file.name)
+    except Exception as e:
+        raise gr.Error(f"Error loading CSV: {str(e)}")
+
+with gr.Blocks(title="AI CSV Analyzer") as demo:
+    gr.Markdown("# 📊 AI-Powered CSV Analyzer")
+    gr.Markdown("Upload a CSV file and ask questions about your data!")
+    
+    with gr.Row():
+        data_state = gr.State()
+        file_input = gr.UploadButton(label="Upload CSV", file_types=[".csv"])
+    
+    chatbot = gr.Chatbot(height=400)
+    msg = gr.Textbox(label="Your Question")
+    clear = gr.ClearButton([msg, chatbot])
+    
+    file_input.upload(
+        fn=handle_file,
+        inputs=file_input,
+        outputs=data_state
+    )
+    
+    msg.submit(
+        fn=analyze_data,
+        inputs=[msg, data_state, chatbot],
+        outputs=chatbot,
+        queue=False
+    )
+
+if __name__ == "__main__":
+    demo.launch()