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.")