From 013ebec675e7d2fc3cd7e01bf35ce117e040f679 Mon Sep 17 00:00:00 2001 From: Deepanshu Gautam Date: Sun, 26 Oct 2025 22:27:35 +0530 Subject: [PATCH 1/2] Delete ai-models/fare_estimation.py --- ai-models/fare_estimation.py | 192 ----------------------------------- 1 file changed, 192 deletions(-) delete mode 100644 ai-models/fare_estimation.py diff --git a/ai-models/fare_estimation.py b/ai-models/fare_estimation.py deleted file mode 100644 index c1ebedb..0000000 --- a/ai-models/fare_estimation.py +++ /dev/null @@ -1,192 +0,0 @@ -import streamlit as st -import folium -from streamlit_folium import st_folium -import pickle -import pandas as pd -from datetime import datetime - -# Sample metro stations with coordinates -stations = { - "Wardha Junction": [20.738, 78.601], - "Indira Chowk": [20.742, 78.623], - "Mahatma Nagar": [20.753, 78.634], - "Wardha Bazaar": [20.756, 78.61], - "IT Park": [20.77, 78.645] -} -station_list = list(stations.keys()) - -# Load ML model with caching -@st.cache_resource -def load_model(): - try: - with open('passenger_flow_model.pkl', 'rb') as f: - model = pickle.load(f) - return model - except FileNotFoundError: - return None - -# Fare calculation logic -def calculate_fare(start, end): - distance = abs(station_list.index(start) - station_list.index(end)) - if distance == 0: - return 0, 0, 0 - base_fare = 10 - extra_fare = 5 * (distance - 1) - total = base_fare + extra_fare - return total, base_fare, extra_fare - -# Travel time estimation -def estimate_time(start, end): - distance = abs(station_list.index(start) - station_list.index(end)) - return distance * 2 # assume 2 minutes per station - -# ML-based crowd prediction -def predict_crowd_level(model, station_name, hour, day_of_week): - """ - Predict crowd level using ML model - - Args: - model: Trained RandomForestRegressor - station_name: Name of the station - hour: Hour of day (0-23) - day_of_week: Day (0=Monday, 6=Sunday) - - Returns: - tuple: (crowd_label, passenger_count) - """ - if model is None: - # Fallback to time-based logic if model not available - if hour in range(7, 10) or hour in range(17, 20): # Peak hours - return "High 🔴", 450 - elif hour in range(10, 17): # Moderate hours - return "Moderate 🟡", 250 - else: # Off-peak - return "Low đŸŸĸ", 100 - - try: - # Create feature dataframe matching model training format - # Adjust features based on your actual model training - station_idx = station_list.index(station_name) - - features = pd.DataFrame({ - 'hour': [hour], - 'day_of_week': [day_of_week], - 'station_id': [station_idx], - 'is_weekend': [1 if day_of_week >= 5 else 0], - 'is_peak_hour': [1 if hour in range(7, 10) or hour in range(17, 20) else 0] - }) - - # Predict passenger count - passenger_count = int(model.predict(features)[0]) - - # Convert to crowd level - if passenger_count < 150: - crowd_label = "Low đŸŸĸ" - elif passenger_count < 300: - crowd_label = "Moderate 🟡" - else: - crowd_label = "High 🔴" - - return crowd_label, passenger_count - - except Exception as e: - st.error(f"Prediction error: {str(e)}") - return "Unknown âšĒ", 0 - -# Streamlit UI -st.set_page_config(page_title="WardhaMetroFlow - Fare Estimator", layout="wide") -st.title("💰 Wardha Metro Fare Estimator") - -# Load model -model = load_model() -if model is None: - st.warning("⚠ī¸ ML model not found. Using time-based crowd estimation as fallback.") - -# Time and day selection -st.sidebar.header("🕐 Travel Time Settings") -current_hour = datetime.now().hour -selected_hour = st.sidebar.slider("Select Hour of Day", 0, 23, current_hour, - help="Choose travel time (24-hour format)") -days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] -current_day = datetime.now().weekday() -selected_day = st.sidebar.selectbox("Select Day of Week", days, index=current_day) -day_of_week = days.index(selected_day) - -# Display selected time -st.sidebar.info(f"📅 {selected_day}, {selected_hour}:00") - -# Station selection -col1, col2 = st.columns(2) -with col1: - start_station = st.selectbox("Select Start Station", station_list) -with col2: - end_station = st.selectbox("Select Destination Station", station_list) - -if start_station and end_station: - total_fare, base_fare, extra_fare = calculate_fare(start_station, end_station) - time_estimate = estimate_time(start_station, end_station) - - # Get crowd predictions for both stations - start_crowd, start_passengers = predict_crowd_level(model, start_station, selected_hour, day_of_week) - end_crowd, end_passengers = predict_crowd_level(model, end_station, selected_hour, day_of_week) - - # Display results - st.success(f"**Route:** {start_station} → {end_station}") - st.info(f"🚌 Stations covered: {abs(station_list.index(start_station) - station_list.index(end_station)) + 1}") - st.success(f"💰 Fare: ₹{total_fare} (Base ₹{base_fare} + Extra ₹{extra_fare})") - st.warning(f"⏱ Estimated Travel Time: {time_estimate} minutes") - - # Display crowd predictions with metrics - st.markdown("### đŸšĻ Real-Time Crowd Predictions") - col_crowd1, col_crowd2 = st.columns(2) - - with col_crowd1: - st.metric( - label=f"🚉 {start_station}", - value=start_crowd, - delta=f"{start_passengers} passengers" - ) - - with col_crowd2: - st.metric( - label=f"🚉 {end_station}", - value=end_crowd, - delta=f"{end_passengers} passengers" - ) - - # Intermediate stations - idx_start = station_list.index(start_station) - idx_end = station_list.index(end_station) - if idx_start < idx_end: - route_stations = station_list[idx_start:idx_end+1] - else: - route_stations = station_list[idx_end:idx_start+1][::-1] - - st.markdown("### 🚏 Intermediate Stations on this Route:") - st.write(" → ".join(route_stations)) - - # Show map - m = folium.Map(location=[20.75, 78.62], zoom_start=13, tiles="CartoDB Positron") - - # Add all stations - for station, coords in stations.items(): - color = "blue" - if station == start_station: - color = "green" - elif station == end_station: - color = "red" - folium.Marker(coords, tooltip=station, icon=folium.Icon(color=color, icon="train")).add_to(m) - - # Highlight route path - route_coords = [stations[s] for s in route_stations] - folium.PolyLine(route_coords, color="purple", weight=5).add_to(m) - - st_folium(m, width=750, height=500) - -# Footer with info -st.markdown("---") -st.markdown("**📊 Powered by Machine Learning** | Predictions based on historical passenger flow data") -if model: - st.success("✅ ML Model Active: Using RandomForestRegressor for accurate crowd predictions") -else: - st.info("ℹī¸ Using time-based heuristics for crowd estimation") \ No newline at end of file From 9d3f0fedb007e849de8a9d86dc20754f5ec48cc0 Mon Sep 17 00:00:00 2001 From: Deepanshu Gautam Date: Sun, 26 Oct 2025 22:28:23 +0530 Subject: [PATCH 2/2] Add files via upload --- ai-models/fare_estimation.py | 192 +++++++++++++++++++++++++++++++++++ 1 file changed, 192 insertions(+) create mode 100644 ai-models/fare_estimation.py diff --git a/ai-models/fare_estimation.py b/ai-models/fare_estimation.py new file mode 100644 index 0000000..c1ebedb --- /dev/null +++ b/ai-models/fare_estimation.py @@ -0,0 +1,192 @@ +import streamlit as st +import folium +from streamlit_folium import st_folium +import pickle +import pandas as pd +from datetime import datetime + +# Sample metro stations with coordinates +stations = { + "Wardha Junction": [20.738, 78.601], + "Indira Chowk": [20.742, 78.623], + "Mahatma Nagar": [20.753, 78.634], + "Wardha Bazaar": [20.756, 78.61], + "IT Park": [20.77, 78.645] +} +station_list = list(stations.keys()) + +# Load ML model with caching +@st.cache_resource +def load_model(): + try: + with open('passenger_flow_model.pkl', 'rb') as f: + model = pickle.load(f) + return model + except FileNotFoundError: + return None + +# Fare calculation logic +def calculate_fare(start, end): + distance = abs(station_list.index(start) - station_list.index(end)) + if distance == 0: + return 0, 0, 0 + base_fare = 10 + extra_fare = 5 * (distance - 1) + total = base_fare + extra_fare + return total, base_fare, extra_fare + +# Travel time estimation +def estimate_time(start, end): + distance = abs(station_list.index(start) - station_list.index(end)) + return distance * 2 # assume 2 minutes per station + +# ML-based crowd prediction +def predict_crowd_level(model, station_name, hour, day_of_week): + """ + Predict crowd level using ML model + + Args: + model: Trained RandomForestRegressor + station_name: Name of the station + hour: Hour of day (0-23) + day_of_week: Day (0=Monday, 6=Sunday) + + Returns: + tuple: (crowd_label, passenger_count) + """ + if model is None: + # Fallback to time-based logic if model not available + if hour in range(7, 10) or hour in range(17, 20): # Peak hours + return "High 🔴", 450 + elif hour in range(10, 17): # Moderate hours + return "Moderate 🟡", 250 + else: # Off-peak + return "Low đŸŸĸ", 100 + + try: + # Create feature dataframe matching model training format + # Adjust features based on your actual model training + station_idx = station_list.index(station_name) + + features = pd.DataFrame({ + 'hour': [hour], + 'day_of_week': [day_of_week], + 'station_id': [station_idx], + 'is_weekend': [1 if day_of_week >= 5 else 0], + 'is_peak_hour': [1 if hour in range(7, 10) or hour in range(17, 20) else 0] + }) + + # Predict passenger count + passenger_count = int(model.predict(features)[0]) + + # Convert to crowd level + if passenger_count < 150: + crowd_label = "Low đŸŸĸ" + elif passenger_count < 300: + crowd_label = "Moderate 🟡" + else: + crowd_label = "High 🔴" + + return crowd_label, passenger_count + + except Exception as e: + st.error(f"Prediction error: {str(e)}") + return "Unknown âšĒ", 0 + +# Streamlit UI +st.set_page_config(page_title="WardhaMetroFlow - Fare Estimator", layout="wide") +st.title("💰 Wardha Metro Fare Estimator") + +# Load model +model = load_model() +if model is None: + st.warning("⚠ī¸ ML model not found. Using time-based crowd estimation as fallback.") + +# Time and day selection +st.sidebar.header("🕐 Travel Time Settings") +current_hour = datetime.now().hour +selected_hour = st.sidebar.slider("Select Hour of Day", 0, 23, current_hour, + help="Choose travel time (24-hour format)") +days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] +current_day = datetime.now().weekday() +selected_day = st.sidebar.selectbox("Select Day of Week", days, index=current_day) +day_of_week = days.index(selected_day) + +# Display selected time +st.sidebar.info(f"📅 {selected_day}, {selected_hour}:00") + +# Station selection +col1, col2 = st.columns(2) +with col1: + start_station = st.selectbox("Select Start Station", station_list) +with col2: + end_station = st.selectbox("Select Destination Station", station_list) + +if start_station and end_station: + total_fare, base_fare, extra_fare = calculate_fare(start_station, end_station) + time_estimate = estimate_time(start_station, end_station) + + # Get crowd predictions for both stations + start_crowd, start_passengers = predict_crowd_level(model, start_station, selected_hour, day_of_week) + end_crowd, end_passengers = predict_crowd_level(model, end_station, selected_hour, day_of_week) + + # Display results + st.success(f"**Route:** {start_station} → {end_station}") + st.info(f"🚌 Stations covered: {abs(station_list.index(start_station) - station_list.index(end_station)) + 1}") + st.success(f"💰 Fare: ₹{total_fare} (Base ₹{base_fare} + Extra ₹{extra_fare})") + st.warning(f"⏱ Estimated Travel Time: {time_estimate} minutes") + + # Display crowd predictions with metrics + st.markdown("### đŸšĻ Real-Time Crowd Predictions") + col_crowd1, col_crowd2 = st.columns(2) + + with col_crowd1: + st.metric( + label=f"🚉 {start_station}", + value=start_crowd, + delta=f"{start_passengers} passengers" + ) + + with col_crowd2: + st.metric( + label=f"🚉 {end_station}", + value=end_crowd, + delta=f"{end_passengers} passengers" + ) + + # Intermediate stations + idx_start = station_list.index(start_station) + idx_end = station_list.index(end_station) + if idx_start < idx_end: + route_stations = station_list[idx_start:idx_end+1] + else: + route_stations = station_list[idx_end:idx_start+1][::-1] + + st.markdown("### 🚏 Intermediate Stations on this Route:") + st.write(" → ".join(route_stations)) + + # Show map + m = folium.Map(location=[20.75, 78.62], zoom_start=13, tiles="CartoDB Positron") + + # Add all stations + for station, coords in stations.items(): + color = "blue" + if station == start_station: + color = "green" + elif station == end_station: + color = "red" + folium.Marker(coords, tooltip=station, icon=folium.Icon(color=color, icon="train")).add_to(m) + + # Highlight route path + route_coords = [stations[s] for s in route_stations] + folium.PolyLine(route_coords, color="purple", weight=5).add_to(m) + + st_folium(m, width=750, height=500) + +# Footer with info +st.markdown("---") +st.markdown("**📊 Powered by Machine Learning** | Predictions based on historical passenger flow data") +if model: + st.success("✅ ML Model Active: Using RandomForestRegressor for accurate crowd predictions") +else: + st.info("ℹī¸ Using time-based heuristics for crowd estimation") \ No newline at end of file