Upload app (5).py

app (5).py

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+import streamlit as st
+import requests
+import firebase_admin
+from firebase_admin import credentials, db, auth
+from PIL import Image
+import numpy as np
+from geopy.geocoders import Nominatim
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.applications.mobilenet_v2 import decode_predictions, preprocess_input
+import json
+
+# Initialize Firebase
+if not firebase_admin._apps:
+    cred = credentials.Certificate("firebase_credentials.json")  
+    firebase_admin.initialize_app(cred, {
+        'databaseURL': 'https://binsight-beda0-default-rtdb.asia-southeast1.firebasedatabase.app/'
+    })
+
+# 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 using geolocation API
+def get_user_location():
+    st.write("Fetching location, please allow location access in your browser.")
+    geolocator = Nominatim(user_agent="binsight")
+    try:
+        ip_info = requests.get("https://ipinfo.io/json").json()
+        loc = ip_info.get("loc", "").split(",")
+        latitude, longitude = loc[0], loc[1] if len(loc) == 2 else (None, None)
+        if latitude and longitude:
+            address = geolocator.reverse(f"{latitude}, {longitude}").address
+            return latitude, longitude, address
+    except Exception as e:
+        st.error(f"Error retrieving location: {e}")
+    return None, None, None
+
+# User Login
+st.sidebar.header("User Login")
+user_email = st.sidebar.text_input("Enter your email")
+login_button = st.sidebar.button("Login")
+
+if login_button:
+    if user_email:
+        st.session_state["user_email"] = user_email
+        st.sidebar.success(f"Logged in as {user_email}")
+
+if "user_email" not in st.session_state:
+    st.warning("Please log in first.")
+    st.stop()
+
+# Get user location and display details
+latitude, longitude, address = get_user_location()
+if latitude and longitude:
+    st.success(f"Location detected: {address}")
+else:
+    st.warning("Unable to fetch location, please ensure location access is enabled.")
+    st.stop()
+
+# Streamlit App
+st.title("BinSight: Upload Dustbin Image")
+
+uploaded_file = st.file_uploader("Upload an image of the dustbin", type=["jpg", "jpeg", "png"])
+submit_button = st.button("Analyze and Upload")
+
+if submit_button and uploaded_file:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+    classification_results = classify_image_with_mobilenet(image)
+    
+    if classification_results:
+        db_ref = db.reference("dustbins")
+        dustbin_data = {
+            "user_email": st.session_state["user_email"],
+            "latitude": latitude,
+            "longitude": longitude,
+            "address": address,
+            "classification": classification_results,
+            "allocated_truck": None,
+            "status": "Pending"
+        }
+        db_ref.push(dustbin_data)
+        st.success("Dustbin data uploaded successfully!")
+        st.write(f"**Location:** {address}")
+        st.write(f"**Latitude:** {latitude}, **Longitude:** {longitude}")
+    else:
+        st.error("Missing classification details. Cannot upload.")
+
+
+
+
+
+
+
+# best with firebase but below code is not giving correct location of user.
+
+# import streamlit as st
+# import requests
+# import firebase_admin
+# from firebase_admin import credentials, db, auth
+# from PIL import Image
+# import numpy as np
+# from geopy.geocoders import Nominatim
+# from tensorflow.keras.applications import MobileNetV2
+# from tensorflow.keras.applications.mobilenet_v2 import decode_predictions, preprocess_input
+
+# # Initialize Firebase
+# if not firebase_admin._apps:
+#     cred = credentials.Certificate("firebase_credentials.json")  
+#     firebase_admin.initialize_app(cred, {
+#         'databaseURL': 'https://binsight-beda0-default-rtdb.asia-southeast1.firebasedatabase.app/'
+#     })
+
+# # 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
+
+# # User Login
+# st.sidebar.header("User Login")
+# user_email = st.sidebar.text_input("Enter your email")
+# login_button = st.sidebar.button("Login")
+
+# if login_button:
+#     if user_email:
+#         st.session_state["user_email"] = user_email
+#         st.sidebar.success(f"Logged in as {user_email}")
+
+# if "user_email" not in st.session_state:
+#     st.warning("Please log in first.")
+#     st.stop()
+
+# # Streamlit App
+# st.title("BinSight: Upload Dustbin Image")
+
+# uploaded_file = st.file_uploader("Upload an image of the dustbin", type=["jpg", "jpeg", "png"])
+# submit_button = st.button("Analyze and Upload")
+
+# if submit_button and uploaded_file:
+#     image = Image.open(uploaded_file)
+#     st.image(image, caption="Uploaded Image", use_container_width=True)
+
+#     classification_results = classify_image_with_mobilenet(image)
+#     latitude, longitude, address = get_user_location()
+
+#     if latitude and longitude and classification_results:
+#         db_ref = db.reference("dustbins")
+#         dustbin_data = {
+#             "user_email": st.session_state["user_email"],
+#             "latitude": latitude,
+#             "longitude": longitude,
+#             "address": address,
+#             "classification": classification_results,
+#             "allocated_truck": None,
+#             "status": "Pending"
+#         }
+#         db_ref.push(dustbin_data)
+#         st.success("Dustbin data uploaded successfully!")
+#     else:
+#         st.error("Missing classification or location details. Cannot upload.")
+
+
+
+
+
+
+
+
+
+
+
+# Below is the old version but it is without of firebase and here is the addition of gemini.
+
+# 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.")