Delete app.py

app.py

@@ -1,93 +0,0 @@
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-from datetime import datetime
-from model import predict_delay
-from utils import validate_inputs, generate_heatmap
-
-# Streamlit app configuration
-st.set_page_config(page_title="Delay 🚀", layout="wide")
-
-# Title
-st.title("Project Delay Predictor 🚀")
-
-# Task options per phase
-task_options = {
-    "Planning": ["Define Scope", "Resource Allocation", "Permit Acquisition"],
-    "Design": ["Architectural Drafting", "Engineering Analysis", "Design Review"],
-    "Construction": ["Foundation Work", "Structural Build", "Utility Installation"]
-}
-
-# Input form
-with st.form("project_form"):
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        project_name = st.text_input("Project Name")
-        phase = st.selectbox("Phase", [""] + ["Planning", "Design", "Construction"], index=0)
-        task = st.selectbox("Task", [""] + (task_options.get(phase, []) if phase else []), index=0)
-        current_progress = st.number_input("Current Progress (%)", min_value=0.0, max_value=100.0, step=1.0)
-        task_expected_duration = st.number_input("Task Expected Duration (days)", min_value=0, step=1)
-        task_actual_duration = st.number_input("Task Actual Duration (days)", min_value=0, step=1)
-    
-    with col2:
-        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)
-        weather_impact_score = st.number_input("Weather Impact Score (0-100)", min_value=0, max_value=100, step=1)
-        weather_condition = st.text_input("Weather Condition (e.g., Rain)")
-        weather_forecast_date = st.date_input("Weather Forecast Date", min_value=datetime(2025, 1, 1))
-    
-    submit_button = st.form_submit_button("Predict Delay")
-    pdf_button = st.form_submit_button("Generate PDF Report")
-
-# Process form submission
-if submit_button:
-    # Prepare input data
-    input_data = {
-        "project_name": project_name,
-        "phase": phase,
-        "task": task,
-        "current_progress": current_progress,
-        "task_expected_duration": task_expected_duration,
-        "task_actual_duration": task_actual_duration,
-        "workforce_gap": workforce_gap,
-        "workforce_skill_level": workforce_skill_level,
-        "workforce_shift_hours": workforce_shift_hours,
-        "weather_impact_score": weather_impact_score,
-        "weather_condition": weather_condition,
-        "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(f"**High Risk Phases**: {prediction['high_risk_phases']}")
-            st.write(f"**AI Insights**: {prediction['ai_insights']}")
-            
-            # Generate and display heatmap
-            fig = generate_heatmap(prediction['delay_probability'], f"{phase}: {task}")
-            st.pyplot(fig)
-            
-            # Store prediction in session state for PDF
-            st.session_state.prediction = prediction
-            st.session_state.input_data = input_data
-
-# Generate PDF report
-if pdf_button:
-    if 'prediction' not in st.session_state:
-        st.error("Generate a prediction first")
-    else:
-        st.write("PDF generation is not fully implemented in this demo. Use browser print functionality as a workaround.")