Update app.py

app.py

@@ -9,55 +9,6 @@ from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image
 from reportlab.lib.styles import getSampleStyleSheet
 from reportlab.lib.units import inch
 from io import BytesIO
-import 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")
@@ -78,15 +29,6 @@ if 'phase' not in st.session_state:
 if 'task' not in st.session_state:
     st.session_state.task = ""
 
-# Function to format high_risk_phases with flag and alert
-def format_high_risk_phases(high_risk_phases):
-    formatted = []
-    for phase in high_risk_phases:
-        flag = "🚩" if phase['risk'] > 75 else ""
-        alert = "[Alert]" if phase['risk'] > 75 else ""
-        formatted.append(f"{flag} {phase['phase']}: {phase['task']} (Risk: {phase['risk']:.1f}%) {alert}")
-    return formatted
-
 # Function to generate PDF
 def generate_pdf(input_data, prediction, heatmap_fig):
     buffer = BytesIO()
@@ -108,6 +50,8 @@ 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']}"
@@ -118,10 +62,9 @@ def generate_pdf(input_data, prediction, heatmap_fig):
 
     # Prediction Results
     story.append(Paragraph("Prediction Results", styles['Heading2']))
-    high_risk_text = "<br/>".join(format_high_risk_phases(prediction['high_risk_phases']))
     prediction_fields = [
         f"Delay Probability: {prediction['delay_probability']:.2f}%",
-        f"High Risk Phases:<br/>{high_risk_text}",
+        f"High Risk Phases: {prediction['high_risk_phases']}",
         f"AI Insights: {prediction['ai_insights']}",
         f"Weather Condition: {prediction['weather_condition']}"
     ]
@@ -131,11 +74,13 @@ def generate_pdf(input_data, prediction, heatmap_fig):
 
     # Heatmap
     story.append(Paragraph("Delay Risk Heatmap", styles['Heading2']))
+    # Save heatmap figure to BytesIO as PNG
     img_buffer = BytesIO()
     heatmap_fig.savefig(img_buffer, format='png', bbox_inches='tight')
     img_buffer.seek(0)
+    # Embed image in PDF (scale to fit page width)
     story.append(Image(img_buffer, width=6*inch, height=2*inch))
-    plt.close(heatmap_fig)
+    plt.close(heatmap_fig)  # Close figure to free memory
 
     doc.build(story)
     buffer.seek(0)
@@ -149,9 +94,10 @@ with st.form("project_form"):
         project_name = st.text_input("Project Name")
         phase = st.selectbox("Phase", [""] + ["Planning", "Design", "Construction"], index=0, key="phase_select")
         
+        # Update task options based on phase
         if phase != st.session_state.phase:
             st.session_state.phase = phase
-            st.session_state.task = ""
+            st.session_state.task = ""  # Reset task when phase changes
         task_options_list = [""] + task_options.get(phase, []) if phase else [""]
         task = st.selectbox("Task", task_options_list, index=0, key="task_select")
         current_progress = st.number_input("Current Progress (%)", min_value=0.0, max_value=100.0, step=1.0)
@@ -162,8 +108,10 @@ with st.form("project_form"):
         workforce_gap = st.number_input("Workforce Gap (%)", min_value=0.0, max_value=100.0, step=1.0)
         workforce_skill_level = st.selectbox("Workforce Skill Level", ["Low", "Medium", "High"], index=1)
         workforce_shift_hours = st.number_input("Workforce Shift Hours", min_value=0, step=1, value=8)
+        # Debug output for shift hours
         st.write(f"**Selected Shift Hours**: {workforce_shift_hours}")
         weather_impact_score = st.number_input("Weather Impact Score (0-100)", min_value=0, max_value=100, step=1)
+        # Display computed weather condition
         weather_condition = get_weather_condition(weather_impact_score)
         st.write(f"**Weather Condition**: {weather_condition}")
         weather_forecast_date = st.date_input("Weather Forecast Date", min_value=datetime(2025, 1, 1))
@@ -172,6 +120,7 @@ with st.form("project_form"):
 
 # Process form submission
 if submit_button:
+    # Prepare input data
     input_data = {
         "project_name": project_name,
         "phase": phase,
@@ -187,27 +136,30 @@ if submit_button:
         "weather_forecast_date": weather_forecast_date.strftime("%Y-%m-%d")
     }
     
+    # Validate inputs
     error = validate_inputs(input_data)
     if error:
         st.error(error)
     else:
+        # Get prediction
         with st.spinner("Predicting..."):
             prediction = predict_delay(input_data)
         
         if "error" in prediction:
             st.error(prediction["error"])
         else:
+            # Display results
             st.subheader("Prediction Results")
             st.write(f"**Delay Probability**: {prediction['delay_probability']:.2f}%")
-            st.write("**High Risk Phases**:")
-            for line in format_high_risk_phases(prediction['high_risk_phases']):
-                st.write(line)
+            st.write(f"**High Risk Phases**: {prediction['high_risk_phases']}")
             st.write(f"**AI Insights**: {prediction['ai_insights']}")
             st.write(f"**Weather Condition**: {prediction['weather_condition']}")
             
+            # Generate and display heatmap
             fig = generate_heatmap(prediction['delay_probability'], f"{phase}: {task}")
             st.pyplot(fig)
             
+            # Generate PDF with heatmap and provide download link
             pdf_buffer = generate_pdf(input_data, prediction, fig)
             st.download_button(
                 label="Download Prediction Report (PDF)",
@@ -216,9 +168,6 @@ if submit_button:
                 mime="application/pdf"
             )
             
+            # Store prediction in session state
             st.session_state.prediction = prediction
-            st.session_state.input_data = input_data
-
-# Commented out FastAPI server startup
-# if __name__ == "__main__":
-#     uvicorn.run(app, host="0.0.0.0", port=8000, log_level="error")
+            st.session_state.input_data = input_data