Update app.py

app.py

@@ -1,28 +1,13 @@
+import ast
 
-
-import gradio as gr
-from smolagents import HfApiModel, CodeAgent
-from huggingface_hub import login
-import os
-import shutil
-
-from huggingface_hub import login
-
-# Add this before model initialization
-hf_token = os.getenv("HF_TOKEN")
-login(token=hf_token, add_to_git_credential=True)
-
-# Then create model with explicit token
-model = HfApiModel(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    token=hf_token  # Pass token explicitly
-)
-
-# Add this formatting function
 def format_analysis_report(raw_output, visuals):
     try:
-        # Convert string output to dictionary
-        analysis_dict = ast.literal_eval(str(raw_output))
+        # Check if raw_output is already a dictionary
+        if isinstance(raw_output, dict):
+            analysis_dict = raw_output
+        else:
+            # If it's a string, try to convert it to a dictionary
+            analysis_dict = ast.literal_eval(str(raw_output))
         
         report = f"""
         <div style="font-family: Arial, sans-serif; padding: 20px; color: #333;">
@@ -40,98 +25,6 @@ def format_analysis_report(raw_output, visuals):
         </div>
         """
         return report, visuals
-    except:
-        return raw_output, visuals
-
-def format_observations(observations):
-    items = []
-    for key, value in observations.items():
-        if 'proportions' in key:
-            items.append(f"""
-            <div style="margin: 15px 0; padding: 15px; background: white; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.05);">
-                <h3 style="margin: 0 0 10px 0; color: #4A708B;">{key.replace('_', ' ').title()}</h3>
-                <pre style="margin: 0; padding: 10px; background: #f8f9fa; border-radius: 4px;">{value}</pre>
-            </div>
-            """)
-    return '\n'.join(items)
-
-def format_insights(insights, visuals):
-    items = []
-    for idx, (key, insight) in enumerate(insights.items()):
-        img_tag = ""
-        if idx < len(visuals):
-            img_tag = f'<img src="/file={visuals[idx]}" style="max-width: 100%; height: auto; margin-top: 10px; border-radius: 6px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">'
-        
-        items.append(f"""
-        <div style="margin: 20px 0; padding: 20px; background: white; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.05);">
-            <div style="display: flex; align-items: center; gap: 10px;">
-                <div style="background: #2B547E; color: white; width: 30px; height: 30px; border-radius: 50%; display: flex; align-items: center; justify-content: center;">{idx+1}</div>
-                <p style="margin: 0; font-size: 16px;">{insight}</p>
-            </div>
-            {img_tag}
-        </div>
-        """)
-    return '\n'.join(items)
-
-def analyze_data(csv_file, additional_notes=""):
-    # Clear previous figures
-    if os.path.exists('./figures'):
-        shutil.rmtree('./figures')
-    os.makedirs('./figures', exist_ok=True)
-
-    # Initialize model and agent
-    model = HfApiModel("mistralai/Mixtral-8x7B-Instruct-v0.1")  # Best overall
-    agent = CodeAgent(
-    tools=[],
-    model=model,
-    additional_authorized_imports=[
-        "numpy",
-        "pandas", 
-        "matplotlib.pyplot",
-        "seaborn"
-    ],
-    )
-    # Run analysis
-    analysis_result = agent.run(
-        """You are an expert data analyst. Perform comprehensive analysis including:
-        1. Basic statistics and data quality checks
-        2. 3 insightful analytical questions about relationships in the data
-        3. Visualization of key patterns and correlations
-        4. Actionable real-world insights derived from findings
-        
-        Generate publication-quality visualizations and save to './figures/'
-        """,
-        additional_args={
-            "additional_notes": additional_notes,
-            "source_file": csv_file
-        }
-    )
-
-    # Collect generated visuals
-    visuals = [os.path.join('./figures', f) for f in os.listdir('./figures') 
-              if f.endswith(('.png', '.jpg', '.jpeg'))]
-
-    
-    return format_analysis_report(analysis_result, visuals)
-
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 📊 AI Data Analysis Agent")
-    
-    with gr.Row():
-        with gr.Column():
-            file_input = gr.File(label="Upload CSV Dataset", type="filepath")
-            notes_input = gr.Textbox(label="Dataset Notes (Optional)", lines=3)
-            analyze_btn = gr.Button("Analyze", variant="primary")
-        
-        with gr.Column():
-            analysis_output = gr.Markdown("### Analysis results will appear here...")
-            gallery = gr.Gallery(label="Data Visualizations", columns=2)
-
-    analyze_btn.click(
-        fn=analyze_data,
-        inputs=[file_input, notes_input],
-        outputs=[analysis_output, gallery]
-    )
-
-demo.launch(debug=True)
+    except Exception as e:
+        print(f"Error formatting report: {e}")
+        return raw_output, visuals