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app.py

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+import streamlit as st
+import os
+from PIL import Image
+import numpy as np
+from io import BytesIO
+from dotenv import load_dotenv
+from geopy.geocoders import Nominatim
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.applications.mobilenet_v2 import decode_predictions, preprocess_input
+import requests
+import google.generativeai as genai
+
+# Load environment variables
+load_dotenv()
+
+# Configure Generative AI
+genai.configure(api_key='AIzaSyBREh8Uei7uDCbzPaYW2WdalOdjVWcQLAM')
+
+# Load MobileNetV2 pre-trained model
+mobilenet_model = MobileNetV2(weights="imagenet")
+
+# Function to classify the uploaded image using MobileNetV2
+def classify_image_with_mobilenet(image):
+    try:
+        img = image.resize((224, 224))
+        img_array = np.array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = preprocess_input(img_array)
+        predictions = mobilenet_model.predict(img_array)
+        labels = decode_predictions(predictions, top=5)[0]
+        return {label[1]: float(label[2]) for label in labels}
+    except Exception as e:
+        st.error(f"Error during image classification: {e}")
+        return {}
+
+# Function to get user's location
+def get_user_location():
+    try:
+        ip_info = requests.get("https://ipinfo.io/json").json()
+        location = ip_info.get("loc", "").split(",")
+        latitude = location[0] if len(location) > 0 else None
+        longitude = location[1] if len(location) > 1 else None
+
+        if latitude and longitude:
+            geolocator = Nominatim(user_agent="binsight")
+            address = geolocator.reverse(f"{latitude}, {longitude}").address
+            return latitude, longitude, address
+        return None, None, None
+    except Exception as e:
+        st.error(f"Unable to get location: {e}")
+        return None, None, None
+
+# Function to get nearest municipal details with contact info
+def get_nearest_municipal_details(latitude, longitude):
+    try:
+        if latitude and longitude:
+            # Simulating municipal service retrieval
+            municipal_services = [
+                {"latitude": "12.9716", "longitude": "77.5946", "office": "Bangalore Municipal Office", "phone": "+91-80-12345678"},
+                {"latitude": "28.7041", "longitude": "77.1025", "office": "Delhi Municipal Office", "phone": "+91-11-98765432"},
+                {"latitude": "19.0760", "longitude": "72.8777", "office": "Mumbai Municipal Office", "phone": "+91-22-22334455"},
+            ]
+
+            # Find the nearest municipal service (mock logic: matching first two decimal points)
+            for service in municipal_services:
+                if str(latitude).startswith(service["latitude"][:5]) and str(longitude).startswith(service["longitude"][:5]):
+                    return f"""
+                    **Office**: {service['office']}  
+                    **Phone**: {service['phone']}  
+                    """
+            return "No nearby municipal office found. Please check manually."
+        else:
+            return "Location not available. Unable to fetch municipal details."
+    except Exception as e:
+        st.error(f"Unable to fetch municipal details: {e}")
+        return None
+
+# Function to interact with Generative AI
+def get_genai_response(classification_results, location):
+    try:
+        classification_summary = "\n".join([f"{label}: {score:.2f}" for label, score in classification_results.items()])
+        location_summary = f"""
+        Latitude: {location[0] if location[0] else 'N/A'}
+        Longitude: {location[1] if location[1] else 'N/A'}
+        Address: {location[2] if location[2] else 'N/A'}
+        """
+        prompt = f"""
+        ### You are an environmental expert. Analyze the following:
+        1. **Image Classification**:
+           - {classification_summary}
+        2. **Location**:
+           - {location_summary}
+
+        ### Output Required:
+        1. Detailed insights about the waste detected in the image.
+        2. Specific health risks associated with the detected waste type.
+        3. Precautions to mitigate these health risks.
+        4. Recommendations for proper disposal.
+        """
+        model = genai.GenerativeModel('gemini-pro')
+        response = model.generate_content(prompt)
+        return response
+    except Exception as e:
+        st.error(f"Error using Generative AI: {e}")
+        return None
+
+# Function to display Generative AI response
+def display_genai_response(response):
+    st.subheader("Detailed Analysis and Recommendations")
+    if response and response.candidates:
+        response_content = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts else ""
+        st.write(response_content)
+    else:
+        st.write("No response received from Generative AI or quota exceeded.")
+
+# Streamlit App
+st.title("BinSight: AI-Powered Dustbin and Waste Analysis System")
+st.text("Upload a dustbin image and get AI-powered analysis of the waste and associated health recommendations.")
+
+uploaded_file = st.file_uploader("Upload an image of the dustbin", type=["jpg", "jpeg", "png"], help="Upload a clear image of a dustbin for analysis.")
+submit_button = st.button("Analyze Dustbin")
+
+if submit_button:
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_container_width =True)
+
+        # Classify the image using MobileNetV2
+        st.subheader("Image Classification")
+        classification_results = classify_image_with_mobilenet(image)
+        for label, score in classification_results.items():
+            st.write(f"- **{label}**: {score:.2f}")
+
+        # Get user location
+        location = get_user_location()
+        latitude, longitude, address = location
+
+        st.subheader("User Location")
+        st.write(f"Latitude: {latitude if latitude else 'N/A'}")
+        st.write(f"Longitude: {longitude if longitude else 'N/A'}")
+        st.write(f"Address: {address if address else 'N/A'}")
+
+        # Get nearest municipal details with contact info
+        st.subheader("Nearest Municipal Details")
+        municipal_details = get_nearest_municipal_details(latitude, longitude)
+        st.write(municipal_details)
+
+        # Generate detailed analysis with Generative AI
+        if classification_results:
+            response = get_genai_response(classification_results, location)
+            display_genai_response(response)
+    else:
+        st.write("Please upload an image for analysis.")
+
+
+
+
+
+
+
+
+
+
+
+# import streamlit as st
+# import os
+# from PIL import Image
+# import numpy as np
+# from io import BytesIO
+# from dotenv import load_dotenv
+# from geopy.geocoders import Nominatim
+# from tensorflow.keras.applications import MobileNetV2
+# from tensorflow.keras.applications.mobilenet_v2 import decode_predictions, preprocess_input
+# import requests
+# import google.generativeai as genai
+
+# # Load environment variables
+# load_dotenv()
+
+# # Configure Generative AI
+# genai.configure(api_key='AIzaSyBREh8Uei7uDCbzPaYW2WdalOdjVWcQLAM')
+
+# # Load MobileNetV2 pre-trained model
+# mobilenet_model = MobileNetV2(weights="imagenet")
+
+# # Function to classify the uploaded image using MobileNetV2
+# def classify_image_with_mobilenet(image):
+#     try:
+#         # Resize the image to the input size of MobileNetV2
+#         img = image.resize((224, 224))
+#         img_array = np.array(img)
+#         img_array = np.expand_dims(img_array, axis=0)
+#         img_array = preprocess_input(img_array)
+
+#         # Predict using the MobileNetV2 model
+#         predictions = mobilenet_model.predict(img_array)
+#         labels = decode_predictions(predictions, top=5)[0]
+#         return {label[1]: float(label[2]) for label in labels}
+#     except Exception as e:
+#         st.error(f"Error during image classification: {e}")
+#         return {}
+
+# # Function to get user's location
+# def get_user_location():
+#     try:
+#         # Fetch location using the IPInfo API
+#         ip_info = requests.get("https://ipinfo.io/json").json()
+#         location = ip_info.get("loc", "").split(",")
+#         latitude = location[0] if len(location) > 0 else None
+#         longitude = location[1] if len(location) > 1 else None
+
+#         if latitude and longitude:
+#             geolocator = Nominatim(user_agent="binsight")
+#             address = geolocator.reverse(f"{latitude}, {longitude}").address
+#             return latitude, longitude, address
+#         return None, None, None
+#     except Exception as e:
+#         st.error(f"Unable to get location: {e}")
+#         return None, None, None
+
+# # Function to get nearest municipal details
+# def get_nearest_municipal_details(latitude, longitude):
+#     try:
+#         if latitude and longitude:
+#             # Simulating municipal service retrieval
+#             return f"The nearest municipal office is at ({latitude}, {longitude}). Please contact your local authority for waste management services."
+#         else:
+#             return "Location not available. Unable to fetch municipal details."
+#     except Exception as e:
+#         st.error(f"Unable to fetch municipal details: {e}")
+#         return None
+
+# # Function to interact with Generative AI
+# def get_genai_response(classification_results, location):
+#     try:
+#         # Construct prompt for Generative AI
+#         classification_summary = "\n".join([f"{label}: {score:.2f}" for label, score in classification_results.items()])
+#         location_summary = f"""
+#         Latitude: {location[0] if location[0] else 'N/A'}
+#         Longitude: {location[1] if location[1] else 'N/A'}
+#         Address: {location[2] if location[2] else 'N/A'}
+#         """
+#         prompt = f"""
+#         ### You are an environmental expert. Analyze the following:
+#         1. **Image Classification**:
+#            - {classification_summary}
+#         2. **Location**:
+#            - {location_summary}
+
+#         ### Output Required:
+#         1. Detailed insights about the waste detected in the image.
+#         2. Specific health risks associated with the detected waste type.
+#         3. Precautions to mitigate these health risks.
+#         4. Recommendations for proper disposal.
+#         """
+
+#         model = genai.GenerativeModel('gemini-pro')
+#         response = model.generate_content(prompt)
+#         return response
+#     except Exception as e:
+#         st.error(f"Error using Generative AI: {e}")
+#         return None
+
+# # Function to display Generative AI response
+# def display_genai_response(response):
+#     st.subheader("Detailed Analysis and Recommendations")
+#     if response and response.candidates:
+#         response_content = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts else ""
+#         st.write(response_content)
+#     else:
+#         st.write("No response received from Generative AI or quota exceeded.")
+
+# # Streamlit App
+# st.title("BinSight: AI-Powered Dustbin and Waste Analysis System")
+# st.text("Upload a dustbin image and get AI-powered analysis of the waste and associated health recommendations.")
+
+# uploaded_file = st.file_uploader("Upload an image of the dustbin", type=["jpg", "jpeg", "png"], help="Upload a clear image of a dustbin for analysis.")
+# submit_button = st.button("Analyze Dustbin")
+
+# if submit_button:
+#     if uploaded_file is not None:
+#         image = Image.open(uploaded_file)
+#         st.image(image, caption="Uploaded Image", use_column_width=True)
+
+#         # Classify the image using MobileNetV2
+#         st.subheader("Image Classification")
+#         classification_results = classify_image_with_mobilenet(image)
+#         for label, score in classification_results.items():
+#             st.write(f"- **{label}**: {score:.2f}")
+
+#         # Get user location
+#         location = get_user_location()
+#         latitude, longitude, address = location
+
+#         st.subheader("User Location")
+#         st.write(f"Latitude: {latitude if latitude else 'N/A'}")
+#         st.write(f"Longitude: {longitude if longitude else 'N/A'}")
+#         st.write(f"Address: {address if address else 'N/A'}")
+
+#         # Get nearest municipal details
+#         st.subheader("Nearest Municipal Details")
+#         municipal_details = get_nearest_municipal_details(latitude, longitude)
+#         st.write(municipal_details)
+
+#         # Generate detailed analysis with Generative AI
+#         if classification_results:
+#             response = get_genai_response(classification_results, location)
+#             display_genai_response(response)
+#     else:
+#         st.write("Please upload an image for analysis.")

binsight_visionapi.json

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+{
+  "type": "service_account",
+  "project_id": "binsight-448310",
+  "private_key_id": "86450c821de32c31839542e219a2899ea963db0b",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\npvslRldo1F5OR8kZUZbg/wtk\n-----END PRIVATE KEY-----\n",
+  "client_email": "binsight@binsight-448310.iam.gserviceaccount.com",
+  "client_id": "117574259544737739176",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/binsight%40binsight-448310.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

requirements.txt

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+streamlit
+google.generativeai
+python-dotenv
+pillow
+opencv-python-headless
+numpy
+requests
+geopy
+google-cloud-vision
+tensorflow