Create app.py

app.py

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+import gradio as gr
+import cv2
+import torch
+import numpy as np
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
+import requests
+import os
+
+api_key = os.getenv("WEATHER_API_KEY")
+
+# Load Pre-trained YOLOv5 Model from Hugging Face
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # YOLOv5 small model
+
+# Google Sheets Setup
+def setup_google_sheets(sheet_name):
+    scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+    creds = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
+    client = gspread.authorize(creds)
+    sheet = client.open(sheet_name).sheet1
+    return sheet
+
+sheet = setup_google_sheets("TrafficManagementData")
+
+# Analyze Traffic with YOLOv5
+def analyze_traffic(image):
+    # Perform vehicle detection
+    results = model(image)
+    detected_objects = results.pandas().xyxy[0]
+    vehicle_count = len(detected_objects[detected_objects['name'].isin(['car', 'truck', 'bus', 'motorbike'])])
+    
+    # Predict congestion level based on vehicle count
+    congestion_level = "High" if vehicle_count > 150 else "Medium" if vehicle_count > 75 else "Low"
+    flow_rate = "Slow" if congestion_level == "High" else "Moderate" if congestion_level == "Medium" else "Smooth"
+    return vehicle_count, congestion_level, flow_rate
+
+# Optimize Signal Timing
+def optimize_signal(vehicle_count, congestion_level):
+    if congestion_level == "High":
+        return "Green for 40s, Red for 20s"
+    elif congestion_level == "Medium":
+        return "Green for 30s, Red for 30s"
+    return "Green for 20s, Red for 40s"
+
+# Get Weather Impact on Traffic
+def get_weather_impact(api_key):
+    url = f"http://api.openweathermap.org/data/2.5/weather?q=New+York&appid={api_key}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        weather = data['weather'][0]['description']
+        if "rain" in weather or "snow" in weather:
+            return "Traffic is likely to be slower due to weather conditions."
+        return "Weather is clear, no major impact on traffic."
+    return "Error fetching weather data."
+
+# Process Traffic Image
+def process_traffic_image(image):
+    # Analyze traffic using YOLO
+    vehicle_count, congestion_level, flow_rate = analyze_traffic(image)
+    
+    # Optimize signal based on analysis
+    signal_timing = optimize_signal(vehicle_count, congestion_level)
+    
+    # Get weather impact
+    weather_impact = get_weather_impact(WEATHER_API_KEY)
+    
+    # Log data into Google Sheets
+    sheet.append_row([vehicle_count, congestion_level, flow_rate, signal_timing, weather_impact])
+    
+    # Provide detailed results
+    return (f"Detected Vehicles: {vehicle_count}\n"
+            f"Congestion Level: {congestion_level}\n"
+            f"Traffic Flow: {flow_rate}\n"
+            f"Signal Timing Suggestion: {signal_timing}\n"
+            f"Weather Impact: {weather_impact}")
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=process_traffic_image,
+    inputs=gr.Image(type="numpy", label="Upload Traffic Image"),
+    outputs="text",
+    title="Enhanced AI-Powered Smart Traffic Management System",
+    description="Upload a traffic image to analyze congestion, optimize signal timing, and predict traffic flow."
+)
+
+if __name__ == "__main__":
+    interface.launch()