Update app.py

app.py

@@ -10,61 +10,54 @@ from reportlab.lib.styles import getSampleStyleSheet
 from reportlab.lib.units import inch
 from io import BytesIO
 import base64
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import uvicorn
-import nest_asyncio
-
-# Apply nest_asyncio to allow FastAPI in Streamlit's event loop
-nest_asyncio.apply()
-
-# FastAPI app for API endpoints
-app = FastAPI()
-
-# Pydantic model for input validation
-class ProjectInput(BaseModel):
-    project_name: str
-    phase: str
-    task: str
-    current_progress: float
-    task_expected_duration: int
-    task_actual_duration: int
-    workforce_gap: float
-    workforce_skill_level: str
-    workforce_shift_hours: int
-    weather_impact_score: int
-    weather_forecast_date: str
-
-# API endpoint for prediction
-@app.post("/predict")
-async def predict(project_input: ProjectInput):
-    input_data = project_input.dict()
-    input_data["weather_condition"] = get_weather_condition(input_data["weather_impact_score"])
-    
-    # Validate inputs
-    error = validate_inputs(input_data)
-    if error:
-        raise HTTPException(status_code=400, detail=error)
-    
-    # Get prediction
-    prediction = predict_delay(input_data)
-    
-    if "error" in prediction:
-        raise HTTPException(status_code=500, detail=prediction["error"])
-    
-    # Generate heatmap
-    fig = generate_heatmap(prediction['delay_probability'], f"{input_data['phase']}: {input_data['task']}")
-    
-    # Generate PDF
-    pdf_buffer = generate_pdf(input_data, prediction, fig)
-    pdf_base64 = base64.b64encode(pdf_buffer.getvalue()).decode('utf-8')
-    plt.close(fig)
-    
-    # Return prediction and PDF
-    return {
-        "prediction": prediction,
-        "pdf_base64": pdf_base64
-    }
+# Removed FastAPI-related imports
+# from fastapi import FastAPI, HTTPException
+# from pydantic import BaseModel
+# import uvicorn
+# import nest_asyncio
+
+# Commented out nest_asyncio and FastAPI setup
+# nest_asyncio.apply()
+# app = FastAPI()
+
+# Commented out Pydantic model and API endpoint
+# class ProjectInput(BaseModel):
+#     project_name: str
+#     phase: str
+#     task: str
+#     current_progress: float
+#     task_expected_duration: int
+#     task_actual_duration: int
+#     workforce_gap: float
+#     workforce_skill_level: str
+#     workforce_shift_hours: int
+#     weather_impact_score: int
+#     weather_forecast_date: str
+
+# @app.post("/predict")
+# async def predict(project_input: ProjectInput):
+#     input_data = project_input.dict()
+#     input_data["weather_condition"] = get_weather_condition(input_data["weather_impact_score"])
+#     
+#     error = validate_inputs(input_data)
+#     if error:
+#         raise HTTPException(status_code=400, detail=error)
+#     
+#     prediction = predict_delay(input_data)
+#     
+#     if "error" in prediction:
+#         raise HTTPException(status_code=500, detail=prediction["error"])
+#     
+#     fig = generate_heatmap(prediction['delay_probability'], f"{input_data['phase']}: {input_data['task']}")
+#     
+#     pdf_buffer = generate_pdf(input_data, prediction, fig)
+#     pdf_base64 = base64.b64encode(pdf_buffer.getvalue()).decode('utf-8')
+#     plt.close(fig)
+#     
+#     return {
+#         "prediction": prediction,
+#         "pdf_base64": pdf_base64
+#     }
 
 # Streamlit app configuration
 st.set_page_config(page_title="Delay 🚀", layout="wide")
@@ -115,8 +108,6 @@ def generate_pdf(input_data, prediction, heatmap_fig):
         f"Task Expected Duration: {input_data['task_expected_duration']} days",
         f"Task Actual Duration: {input_data['task_actual_duration']} days",
         f"Workforce Gap: {input_data['workforce_gap']}%",
-        f"Workforce Skill Level: {input_data['workforce_skill_level']}",
-        f"Workforce Shift Hours: {input_data['workforce_shift_hours']}",
         f"Weather Impact Score: {input_data['weather_impact_score']}",
         f"Weather Condition: {input_data['weather_condition']}",
         f"Weather Forecast Date: {input_data['weather_forecast_date']}"
@@ -228,6 +219,6 @@ if submit_button:
             st.session_state.prediction = prediction
             st.session_state.input_data = input_data
 
-# Run FastAPI server in the background
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000, log_level="error")
+# Commented out FastAPI server startup
+# if __name__ == "__main__":
+#     uvicorn.run(app, host="0.0.0.0", port=8000, log_level="error")