Update app.py

app.py

@@ -1,185 +1,150 @@
-import gradio as gr
-import joblib
-import numpy as np
-import pandas as pd
-from sklearn.preprocessing import StandardScaler
-
-# Load the trained model and scaler
-model = joblib.load('developer_productivity_model.joblib')
-scaler = joblib.load('scaler.joblib')
-
-def predict_productivity(daily_coding_hours, commits_per_day, pull_requests_per_week, 
-                        issues_closed_per_week, active_repos, code_reviews_per_week):
-    """
-    Predict developer productivity score based on numeric features.
-    
-    Parameters:
-    - daily_coding_hours: float (hours spent coding per day)
-    - commits_per_day: int (number of commits per day)
-    - pull_requests_per_week: int (number of pull requests per week)
-    - issues_closed_per_week: int (number of issues closed per week)
-    - active_repos: int (number of active repositories)
-    - code_reviews_per_week: int (number of code reviews per week)
-    
-    Returns:
-    - predicted_score: float (predicted productivity score)
-    """
-    
-    try:
-        # Create feature array
-        features = np.array([[
-            daily_coding_hours,
-            commits_per_day,
-            pull_requests_per_week,
-            issues_closed_per_week,
-            active_repos,
-            code_reviews_per_week
-        ]])
-        
-        # Scale features
-        features_scaled = scaler.transform(features)
-        
-        # Make prediction
-        prediction = model.predict(features_scaled)[0]
-        
-        score = round(prediction, 2)
-        
-        # Add interpretation
-        if score >= 80:
-            interpretation = "🌟 Excellent - High productivity developer!"
-        elif score >= 70:
-            interpretation = "✅ Very Good - Above average productivity!"
-        elif score >= 60:
-            interpretation = "👍 Good - Solid productivity level!"
-        elif score >= 50:
-            interpretation = "⚖️ Average - Room for improvement!"
-        else:
-            interpretation = "📈 Below Average - Consider focusing on key metrics!"
-        
-        return f"{score} - {interpretation}"
-        
-    except Exception as e:
-        return f"Error in prediction: {str(e)}"
-
-# Create Gradio interface
-with gr.Blocks(title="GitHub Developer Productivity Predictor", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🚀 GitHub Developer Productivity Predictor
-        
-        This AI model predicts developer productivity scores based on GitHub activity metrics.
-        Enter your development metrics below to get your predicted productivity score!
-        
-        **Features used for prediction:**
-        - Daily coding hours
-        - Commits per day  
-        - Pull requests per week
-        - Issues closed per week
-        - Active repositories
-        - Code reviews per week
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            daily_hours = gr.Number(
-                label="Daily Coding Hours",
-                value=4.0,
-                minimum=0,
-                maximum=24,
-                info="Average hours spent coding per day"
-            )
-            commits_day = gr.Number(
-                label="Commits Per Day",
-                value=5,
-                minimum=0,
-                maximum=50,
-                info="Average number of commits made per day"
-            )
-            prs_week = gr.Number(
-                label="Pull Requests Per Week",
-                value=4,
-                minimum=0,
-                maximum=20,
-                info="Average number of pull requests created per week"
-            )
-            
-        with gr.Column():
-            issues_week = gr.Number(
-                label="Issues Closed Per Week",
-                value=3,
-                minimum=0,
-                maximum=20,
-                info="Average number of issues resolved per week"
-            )
-            repos = gr.Number(
-                label="Active Repositories",
-                value=5,
-                minimum=0,
-                maximum=50,
-                info="Number of repositories actively contributed to"
-            )
-            reviews_week = gr.Number(
-                label="Code Reviews Per Week",
-                value=3,
-                minimum=0,
-                maximum=20,
-                info="Average number of code reviews performed per week"
-            )
-    
-    with gr.Row():
-        predict_btn = gr.Button("🔮 Predict Productivity Score", variant="primary", size="lg")
-        clear_btn = gr.Button("🔄 Clear", variant="secondary")
-    
-    output = gr.Textbox(
-        label="Productivity Prediction",
-        placeholder="Click 'Predict Productivity Score' to see your result...",
-        interactive=False
-    )
-    
-    # Example buttons
-    gr.Markdown("### 📊 Try these examples:")
-    with gr.Row():
-        high_prod_btn = gr.Button("🌟 High Productivity Developer")
-        avg_prod_btn = gr.Button("⚖️ Average Developer") 
-        beginner_btn = gr.Button("🌱 Beginner Developer")
-    
-    # Event handlers
-    predict_btn.click(
-        fn=predict_productivity,
-        inputs=[daily_hours, commits_day, prs_week, issues_week, repos, reviews_week],
-        outputs=output
-    )
-    
-    clear_btn.click(
-        fn=lambda: [4.0, 5, 4, 3, 5, 3, ""],
-        outputs=[daily_hours, commits_day, prs_week, issues_week, repos, reviews_week, output]
-    )
-    
-    high_prod_btn.click(
-        fn=lambda: [6.0, 10, 8, 6, 8, 5],
-        outputs=[daily_hours, commits_day, prs_week, issues_week, repos, reviews_week]
-    )
-    
-    avg_prod_btn.click(
-        fn=lambda: [4.0, 5, 4, 3, 5, 3],
-        outputs=[daily_hours, commits_day, prs_week, issues_week, repos, reviews_week]
-    )
-    
-    beginner_btn.click(
-        fn=lambda: [3.0, 2, 2, 1, 2, 1],
-        outputs=[daily_hours, commits_day, prs_week, issues_week, repos, reviews_week]
-    )
-
-    gr.Markdown(
-        """
-        ### 📝 About the Model
-        This Random Forest model was trained on GitHub developer activity data and uses 6 key metrics to predict productivity scores (0-100).
-        The model achieves good performance in predicting developer productivity based on quantifiable GitHub activities.
-        
-        **Note**: This is a demonstration model. Actual productivity depends on many factors beyond GitHub metrics.
-        """
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import uvicorn
+import os
+
+# Create FastAPI app
+app = FastAPI(title="Developer Productivity Prediction API", version="1.0.0")
+
+# Load the trained model and scaler
+model = joblib.load('developer_productivity_model.joblib')
+scaler = joblib.load('scaler.joblib')
+
+# Pydantic model for API request
+class ProductivityRequest(BaseModel):
+    daily_coding_hours: float
+    commits_per_day: int
+    pull_requests_per_week: int
+    issues_closed_per_week: int
+    active_repos: int
+    code_reviews_per_week: int
+
+class ProductivityResponse(BaseModel):
+    predicted_score: float
+    status: str
+
+def predict_productivity_core(daily_coding_hours, commits_per_day, pull_requests_per_week, 
+                        issues_closed_per_week, active_repos, code_reviews_per_week):
+    """
+    Core prediction function used by both API and Gradio interface.
+    """
+    try:
+        # Create feature array
+        features = np.array([[
+            daily_coding_hours,
+            commits_per_day,
+            pull_requests_per_week,
+            issues_closed_per_week,
+            active_repos,
+            code_reviews_per_week
+        ]])
+        
+        # Scale features
+        features_scaled = scaler.transform(features)
+        
+        # Make prediction
+        prediction = model.predict(features_scaled)[0]
+        
+        return round(prediction, 2)
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
+
+# FastAPI endpoints
+@app.get("/")
+def read_root():
+    return {"message": "Developer Productivity Prediction API", "status": "active"}
+
+@app.post("/predict", response_model=ProductivityResponse)
+def predict_productivity_api(request: ProductivityRequest):
+    """
+    API endpoint to predict developer productivity score.
+    """
+    try:
+        prediction = predict_productivity_core(
+            request.daily_coding_hours,
+            request.commits_per_day,
+            request.pull_requests_per_week,
+            request.issues_closed_per_week,
+            request.active_repos,
+            request.code_reviews_per_week
+        )
+        
+        return ProductivityResponse(
+            predicted_score=prediction,
+            status="success"
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+def health_check():
+    return {"status": "healthy", "model_loaded": True}
+
+# Gradio interface for web UI
+def predict_productivity_gradio(daily_coding_hours, commits_per_day, pull_requests_per_week, 
+                        issues_closed_per_week, active_repos, code_reviews_per_week):
+    """
+    Gradio wrapper for the prediction function.
+    """
+    try:
+        prediction = predict_productivity_core(
+            daily_coding_hours,
+            commits_per_day,
+            pull_requests_per_week,
+            issues_closed_per_week,
+            active_repos,
+            code_reviews_per_week
+        )
+        
+        return f"Predicted Productivity Score: {prediction}"
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_productivity_gradio,
+    inputs=[
+        gr.Slider(minimum=1, maximum=12, value=4.0, step=0.1, label="Daily Coding Hours"),
+        gr.Slider(minimum=0, maximum=20, value=5, step=1, label="Commits per Day"),
+        gr.Slider(minimum=0, maximum=15, value=4, step=1, label="Pull Requests per Week"),
+        gr.Slider(minimum=0, maximum=15, value=3, step=1, label="Issues Closed per Week"),
+        gr.Slider(minimum=1, maximum=20, value=5, step=1, label="Active Repositories"),
+        gr.Slider(minimum=0, maximum=15, value=3, step=1, label="Code Reviews per Week")
+    ],
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="🚀 Developer Productivity Predictor",
+    description="""
+    ### Predict Developer Productivity Score
+    
+    This model predicts developer productivity based on 6 key metrics:
+    - **Daily Coding Hours**: Time spent actively coding
+    - **Commits per Day**: Average daily code commits
+    - **Pull Requests per Week**: Weekly pull requests created
+    - **Issues Closed per Week**: Weekly issues resolved
+    - **Active Repositories**: Number of repositories worked on
+    - **Code Reviews per Week**: Weekly code reviews performed
+    
+    **API Endpoint**: Use `/predict` with POST request for programmatic access.
+    """,
+    examples=[
+        [4.0, 5, 4, 3, 5, 3],  # Your specified values
+        [6.0, 10, 8, 6, 8, 5], # High productivity example
+        [3.0, 2, 2, 1, 2, 1],  # Beginner example
+    ],
+    theme=gr.themes.Soft()
+)
+
+# Mount Gradio app with FastAPI
+app = gr.mount_gradio_app(app, iface, path="/")
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)