| import streamlit as st | |
| import firebase_admin | |
| from firebase_admin import credentials, db | |
| 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 base64 | |
| from io import BytesIO | |
| # 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 convert image to Base64 | |
| def convert_image_to_base64(image): | |
| buffered = BytesIO() | |
| image.save(buffered, format="PNG") | |
| img_str = base64.b64encode(buffered.getvalue()).decode() | |
| return img_str | |
| # Function to get detailed location info from latitude & longitude | |
| def get_location_details(lat, lon): | |
| try: | |
| geolocator = Nominatim(user_agent="binsight") | |
| location = geolocator.reverse((lat, lon), exactly_one=True) | |
| if location: | |
| address = location.raw.get('address', {}) | |
| return { | |
| "road": address.get("road", "Unknown"), | |
| "city": address.get("city", address.get("town", "Unknown")), | |
| "state": address.get("state", "Unknown"), | |
| "country": address.get("country", "Unknown"), | |
| "full_address": location.address | |
| } | |
| except Exception as e: | |
| st.error(f"Error retrieving location details: {e}") | |
| return {} | |
| # JavaScript to get live location and send it to Streamlit | |
| get_location_js = """ | |
| <script> | |
| function sendLocation() { | |
| navigator.geolocation.getCurrentPosition( | |
| (position) => { | |
| const latitude = position.coords.latitude; | |
| const longitude = position.coords.longitude; | |
| const locationData = latitude + "," + longitude; | |
| fetch('/_stcore/', { | |
| method: 'POST', | |
| headers: {'Content-Type': 'application/json'}, | |
| body: JSON.stringify({latlon: locationData}) | |
| }).then(response => response.json()) | |
| .then(data => console.log("Location sent:", data)); | |
| }, | |
| (error) => { | |
| console.log("Error getting location:", error); | |
| } | |
| ); | |
| } | |
| sendLocation(); | |
| </script> | |
| """ | |
| # Run JavaScript in Streamlit | |
| st.components.v1.html(get_location_js, height=0) | |
| # Capture location from Streamlit session state | |
| if "latlon" not in st.session_state: | |
| st.session_state.latlon = None | |
| # Streamlit App | |
| st.title("BinSight: Upload Dustbin Image") | |
| # Fetch live location | |
| if st.session_state.latlon: | |
| try: | |
| latitude, longitude = map(float, st.session_state.latlon.split(",")) | |
| location_details = get_location_details(latitude, longitude) | |
| st.success(f"Detected Location: {location_details['full_address']}") | |
| st.write(f"**State:** {location_details['state']}") | |
| st.write(f"**City:** {location_details['city']}") | |
| st.write(f"**Road:** {location_details['road']}") | |
| st.write(f"**Country:** {location_details['country']}") | |
| except Exception as e: | |
| st.error(f"Error processing location: {e}") | |
| else: | |
| st.warning("Fetching live location... Please allow location access.") | |
| # File uploader for dustbin images | |
| 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 and st.session_state.latlon: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| # Convert image to Base64 | |
| image_base64 = convert_image_to_base64(image) | |
| # Classify Image | |
| classification_results = classify_image_with_mobilenet(image) | |
| if classification_results: | |
| db_ref = db.reference("dustbins") | |
| dustbin_data = { | |
| "latitude": latitude, | |
| "longitude": longitude, | |
| "location_details": location_details, | |
| "classification": classification_results, | |
| "status": "Pending", | |
| "image": image_base64 # Store image as Base64 string | |
| } | |
| db_ref.push(dustbin_data) | |
| st.success("Dustbin data uploaded successfully!") | |
| else: | |
| st.error("Missing classification details. Cannot upload.") | |
| # best without image | |
| # 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.") | |