| # # # # # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # # # # # llm = ChatGoogleGenerativeAI( | |
| # # # # # model="gemini-1.5-flash", | |
| # # # # # google_api_key='AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE', | |
| # # # # # temperature=0.2) | |
| # # # # # poem = llm.invoke("Write a poem on love for burger") | |
| # # # # # print(poem) | |
| # # # # import streamlit as st | |
| # # # # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # # # # # Set up the AI model | |
| # # # # llm = ChatGoogleGenerativeAI( | |
| # # # # model="gemini-1.5-flash", # Free model | |
| # # # # google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| # # # # temperature=0.5 | |
| # # # # ) | |
| # # # # # Streamlit UI | |
| # # # # st.title("🩺 Healthcare AI Assistant") | |
| # # # # st.write("Ask me anything about health, symptoms, diet, or general medical advice!") | |
| # # # # # User Input | |
| # # # # user_question = st.text_input("Enter your health-related question:") | |
| # # # # # Process User Query | |
| # # # # if st.button("Get Recommendation"): | |
| # # # # if user_question.strip(): | |
| # # # # with st.spinner("Analyzing..."): | |
| # # # # response = llm.invoke(user_question) | |
| # # # # st.success("Recommendation:") | |
| # # # # st.write(response) | |
| # # # # else: | |
| # # # # st.warning("Please enter a question!") | |
| # # # # # Footer | |
| # # # # st.markdown("---") | |
| # # # # st.markdown("💡 *Disclaimer: This AI assistant provides general health information. Always consult a doctor for medical concerns.*") | |
| # # # import streamlit as st | |
| # # # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # # # # Set up AI model | |
| # # # llm = ChatGoogleGenerativeAI( | |
| # # # model="gemini-1.5-flash", # Free model | |
| # # # google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| # # # temperature=0.5 | |
| # # # ) | |
| # # # # Streamlit UI | |
| # # # st.title("🩺 AI Healthcare Learning Assistant") | |
| # # # st.write("Ask me anything about healthcare, symptoms, diet, or medical learning!") | |
| # # # # User Input | |
| # # # user_question = st.text_input("Enter your healthcare question:") | |
| # # # # Function to filter AI disclaimers | |
| # # # def is_valid_response(response): | |
| # # # disclaimers = [ | |
| # # # "I am an AI and cannot give medical advice", | |
| # # # "Seek medical attention", | |
| # # # "Consult a doctor", | |
| # # # "Contact your doctor", | |
| # # # "Go to an emergency room", | |
| # # # ] | |
| # # # return not any(phrase.lower() in response.lower() for phrase in disclaimers) | |
| # # # # Process User Query | |
| # # # if st.button("Get Information"): | |
| # # # if user_question.strip(): | |
| # # # with st.spinner("Analyzing..."): | |
| # # # response = llm.invoke(user_question) | |
| # # # # Check if response is valid | |
| # # # if is_valid_response(response): | |
| # # # st.success("Here is the relevant information:") | |
| # # # st.write(response) | |
| # # # else: | |
| # # # st.warning("AI provided a disclaimer. Trying again...") | |
| # # # # Modify prompt to avoid disclaimers | |
| # # # better_prompt = f"Give a well-explained answer for educational purposes only: {user_question}" | |
| # # # retry_response = llm.invoke(better_prompt) | |
| # # # # Display the retried response if it's valid | |
| # # # if is_valid_response(retry_response): | |
| # # # st.success("Here is the refined information:") | |
| # # # st.write(retry_response) | |
| # # # else: | |
| # # # st.error("Unable to get a useful response. Try rephrasing your question.") | |
| # # # else: | |
| # # # st.warning("Please enter a question!") | |
| # # # # Footer | |
| # # # st.markdown("---") | |
| # # # st.markdown("💡 *This AI provides learning-based medical insights, not actual medical advice.*") | |
| # # import streamlit as st | |
| # # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # # # Set up AI model | |
| # # llm = ChatGoogleGenerativeAI( | |
| # # model="gemini-1.5-flash", # Free model | |
| # # google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| # # temperature=0.5 | |
| # # ) | |
| # # # Streamlit UI | |
| # # st.title("🩺 AI Healthcare Learning Assistant") | |
| # # st.write("Ask me anything about healthcare, symptoms, diet, or medical learning!") | |
| # # # User Input | |
| # # user_question = st.text_input("Enter your healthcare question:") | |
| # # # Function to filter AI disclaimers | |
| # # def is_valid_response(response_text): | |
| # # disclaimers = [ | |
| # # "I am an AI and cannot give medical advice", | |
| # # "Seek medical attention", | |
| # # "Consult a doctor", | |
| # # "Contact your doctor", | |
| # # "Go to an emergency room", | |
| # # ] | |
| # # return not any(phrase.lower() in response_text.lower() for phrase in disclaimers) | |
| # # # Process User Query | |
| # # if st.button("Get Information"): | |
| # # if user_question.strip(): | |
| # # with st.spinner("Analyzing..."): | |
| # # response = llm.invoke(user_question) | |
| # # # Extract the text content from AIMessage | |
| # # response_text = response.content if hasattr(response, "content") else str(response) | |
| # # # Check if response is valid | |
| # # if is_valid_response(response_text): | |
| # # st.success("Here is the relevant information:") | |
| # # st.write(response_text) | |
| # # else: | |
| # # st.warning("AI provided a disclaimer. Trying again...") | |
| # # # Modify prompt to avoid disclaimers | |
| # # better_prompt = f"Give a well-explained answer for educational purposes only: {user_question}" | |
| # # retry_response = llm.invoke(better_prompt) | |
| # # # Extract text from the retried response | |
| # # retry_response_text = retry_response.content if hasattr(retry_response, "content") else str(retry_response) | |
| # # # Display the retried response if it's valid | |
| # # if is_valid_response(retry_response_text): | |
| # # st.success("Here is the refined information:") | |
| # # st.write(retry_response_text) | |
| # # else: | |
| # # st.error("Unable to get a useful response. Try rephrasing your question.") | |
| # # else: | |
| # # st.warning("Please enter a question!") | |
| # # # Footer | |
| # # st.markdown("---") | |
| # # st.markdown("💡 *This AI provides learning-based medical insights, not actual medical advice.*") | |
| # import streamlit as st | |
| # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # # Set up AI model | |
| # llm = ChatGoogleGenerativeAI( | |
| # model="gemini-1.5-flash", # Free model | |
| # google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| # temperature=0.5 | |
| # ) | |
| # # Streamlit UI | |
| # st.title("🩺 AI Healthcare Learning Assistant") | |
| # st.write("Ask about symptoms, medicines, and alternative treatments.") | |
| # # User Input | |
| # user_question = st.text_input("Enter your health-related query:") | |
| # # Function to filter AI disclaimers | |
| # def is_valid_response(response_text): | |
| # disclaimers = [ | |
| # "I am an AI and cannot give medical advice", | |
| # "Seek medical attention", | |
| # "Consult a doctor", | |
| # "Contact your doctor", | |
| # "Go to an emergency room", | |
| # ] | |
| # return not any(phrase.lower() in response_text.lower() for phrase in disclaimers) | |
| # # Process User Query | |
| # if st.button("Get Recommendation"): | |
| # if user_question.strip(): | |
| # # Modify user input to ensure the model focuses on learning-based insights | |
| # formatted_question = f"Without any disclaimer, recommend me medicine for {user_question}. Only for learning purposes, not for treatment. Also, suggest some different ways to cure it." | |
| # with st.spinner("Analyzing..."): | |
| # response = llm.invoke(formatted_question) | |
| # # Extract text content | |
| # response_text = response.content if hasattr(response, "content") else str(response) | |
| # # # Check if response is valid | |
| # if is_valid_response(response_text): | |
| # st.success("Here is the relevant information:") | |
| # st.write(response_text) | |
| # else: | |
| # st.warning("AI provided a disclaimer. Trying again...") | |
| # # Retry with a refined prompt | |
| # better_prompt = f"Its only for leaning purpose not for treatment provide medicine names and alternative treatments for: {user_question}" | |
| # retry_response = llm.invoke(better_prompt) | |
| # # Extract text from retry response | |
| # retry_response_text = retry_response.content if hasattr(retry_response, "content") else str(retry_response) | |
| # # # Display the retried response if valid | |
| # # if is_valid_response(retry_response_text): | |
| # # st.success("Here is the refined information:") | |
| # # st.write(retry_response_text) | |
| # # else: | |
| # # st.error("Unable to get a useful response. Try rephrasing your question.") | |
| # else: | |
| # st.warning("Please enter a question!") | |
| # # Footer | |
| # st.markdown("---") | |
| # st.markdown("💡 *This AI provides learning-based medical insights, not actual medical advice.*") | |
| #------------------------------------------------------------------------start | |
| # import streamlit as st | |
| # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # # Set up AI model | |
| # llm = ChatGoogleGenerativeAI( | |
| # model="gemini-1.5-flash", # Free model | |
| # google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| # temperature=0.5 | |
| # ) | |
| # # Streamlit UI | |
| # st.title("🩺 CureBot: AI-Driven Health Assistant") | |
| # st.write("Welcome to CureBot – Your AI-Driven Health Assistant! Simply enter your symptoms or disease name, and get accurate medicine suggestions instantly. Stay informed, stay healthy!") | |
| # # User Input | |
| # user_question = st.text_input("Type your symptoms or disease name, and let CureBot unlock the right cure for you—fast, smart, and AI-powered") | |
| # # Function to filter AI disclaimers | |
| # def is_valid_response(response_text): | |
| # disclaimers = [ | |
| # "I am an AI and cannot give medical advice", | |
| # "Seek medical attention", | |
| # "Consult a doctor", | |
| # "Contact your doctor", | |
| # "Go to an emergency room", | |
| # ] | |
| # return not any(phrase.lower() in response_text.lower() for phrase in disclaimers) | |
| # # Process User Query | |
| # if st.button("Get Recommendation"): | |
| # if user_question.strip(): | |
| # # Ensure the AI provides both medicine and alternative treatments | |
| # formatted_question = ( | |
| # f"Without any disclaimer, recommend medicine for {user_question}. " | |
| # f"5 medicine names " | |
| # f"Also, provide alternative treatments such as home remedies, lifestyle changes, exercises, or dietary suggestions. " | |
| # f"Only for learning purposes, not for treatment." | |
| # ) | |
| # with st.spinner("Analyzing..."): | |
| # # response = llm.invoke(formatted_question) | |
| # # Extract text content | |
| # response_text = response.content if hasattr(response, "content") else str(response) | |
| # # Check if response is valid | |
| # if is_valid_response(response_text): | |
| # st.success("✨ Analysis complete! Here are the best medicine recommendations for you: 🔽") | |
| # st.write(response_text) | |
| # else: | |
| # st.warning("⚠️ Oops! It looks like the input is unclear or incorrect. Please enter a valid disease name or symptoms to get accurate recommendations") | |
| # # Retry with a refined prompt | |
| # better_prompt = ( | |
| # f"Strictly provide a detailed answer including:\n" | |
| # f"1. Medicine names\n" | |
| # f"2. Home remedies\n" | |
| # f"3. Lifestyle changes\n" | |
| # f"4. Exercises\n" | |
| # f"5. Diet recommendations\n" | |
| # f"Do not include any disclaimers. The response should be clear and structured." | |
| # ) | |
| # retry_response = llm.invoke(better_prompt) | |
| # # Extract text from retry response | |
| # retry_response_text = retry_response.content if hasattr(retry_response, "content") else str(retry_response) | |
| # # Display the retried response if valid | |
| # if is_valid_response(retry_response_text): | |
| # st.success("Here is the refined information:") | |
| # st.write(retry_response_text) | |
| # else: | |
| # st.error("Unable to get a useful response. Try rephrasing your question.") | |
| # else: | |
| # st.warning("Please enter a question!") | |
| # # Emergency Contact Button | |
| # if st.button("Emergency Contact"): | |
| # st.subheader("📞 Emergency Contacts") | |
| # st.write("- 🚑 *Ambulance:* 102") | |
| # st.write("- 🏥 *LPU Hospital:* 18001024432") | |
| # st.write("- 🏥 *National Health Helpline:* 108") | |
| # st.write("- ☎ *COVID-19 Helpline:* 1075") | |
| # st.write("- 🚓 *Police:* 100") | |
| # # Footer | |
| # st.markdown("---") | |
| # st.markdown("🔹 Powered by Mayank, Wasim, Pravishank – Innovating Healthcare with AI! 💡 Your Health, Our Mission. 🚀") | |
| #------------------------------------------------------------------------end | |
| # | |
| # | |
| # | |
| # | |
| # | |
| # | |
| # | |
| # | |
| # | |
| ''' | |
| import streamlit as st | |
| import speech_recognition as sr | |
| from deep_translator import GoogleTranslator | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # Set up AI model | |
| llm = ChatGoogleGenerativeAI( | |
| model="gemini-1.5-flash", | |
| google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| temperature=0.5 | |
| ) | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .big-font { font-size:20px !important; } | |
| .stButton>button { background-color: #ff4b4b; color: white; font-size: 18px; } | |
| .stTextInput>div>div>input { font-size: 16px; } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # UI Setup | |
| # st.image("healthcare_logo.png", width=150) | |
| st.title("🩺 CureBot: AI-Driven Health Assistant") | |
| st.write("Empowering healthcare with AI-driven insights and recommendations!") | |
| # Sidebar Navigation | |
| st.sidebar.title("🔍 Navigation") | |
| option = st.sidebar.radio("Select an option:", ["Home", "Symptom Checker", "Doctor Connect", "Health Stats"]) | |
| translator = GoogleTranslator(source='auto', target='en') | |
| if option == "Home": | |
| user_question = st.text_input("Type your symptoms or disease name:") | |
| if st.button("🎤 Speak Symptoms"): | |
| recognizer = sr.Recognizer() | |
| with sr.Microphone() as source: | |
| st.info("Listening...") | |
| try: | |
| audio = recognizer.listen(source) | |
| user_question = recognizer.recognize_google(audio) | |
| st.success(f"Recognized: {user_question}") | |
| except sr.UnknownValueError: | |
| st.error("Could not understand audio") | |
| except sr.RequestError: | |
| st.error("Error in speech recognition service") | |
| lang = st.selectbox("Select Language", ["English", "Hindi", "Spanish"]) | |
| if lang != "English": | |
| user_question = translator.translate(user_question, src="en", dest=lang.lower()).text | |
| if st.button("Get Recommendation"): | |
| if user_question.strip(): | |
| formatted_question = ( | |
| f"Provide medicine and alternative treatments for {user_question}. " | |
| f"List medicines, home remedies, lifestyle changes, exercises, and diet suggestions." | |
| ) | |
| with st.spinner("Analyzing..."): | |
| response = llm.invoke(formatted_question) | |
| response_text = response.content if hasattr(response, "content") else str(response) | |
| st.success("✨ Analysis complete! Here are your recommendations:") | |
| st.markdown(response_text) | |
| else: | |
| st.warning("Please enter a symptom or disease name!") | |
| elif option == "Symptom Checker": | |
| st.subheader("🔎 AI Symptom Checker") | |
| st.write("Find possible diseases based on symptoms.") | |
| symptoms = st.text_area("Enter symptoms separated by commas:") | |
| if st.button("Check Symptoms"): | |
| symptom_query = f"Analyze these symptoms: {symptoms}. List possible diseases." | |
| response = llm.invoke(symptom_query) | |
| st.write(response.content if hasattr(response, "content") else str(response)) | |
| elif option == "Doctor Connect": | |
| st.subheader("🏥 Find a Doctor Near You") | |
| st.write("Using Google Maps API to find the nearest hospitals and doctors.") | |
| st.write("(Feature Under Development)") | |
| elif option == "Health Stats": | |
| st.subheader("📊 Health Trends & Data") | |
| diseases = ['Diabetes', 'Hypertension', 'Heart Disease', 'Asthma', 'Obesity'] | |
| cases = np.random.randint(5000, 20000, size=len(diseases)) | |
| fig, ax = plt.subplots() | |
| ax.barh(diseases, cases, color=['blue', 'green', 'red', 'purple', 'orange']) | |
| ax.set_xlabel("Number of Cases") | |
| ax.set_title("Disease Prevalence Statistics") | |
| st.pyplot(fig) | |
| # Emergency Contact Section | |
| st.sidebar.markdown("---") | |
| st.sidebar.subheader("📞 Emergency Contacts") | |
| st.sidebar.write("- 🚑 *Ambulance:* 102") | |
| st.sidebar.write("- 🏥 *LPU Hospital:* 18001024432") | |
| st.sidebar.write("- 🏥 *National Health Helpline:* 108") | |
| st.sidebar.write("- ☎ *COVID-19 Helpline:* 1075") | |
| st.sidebar.write("- 🚓 *Police:* 1000") | |
| st.markdown("---") | |
| st.markdown("🔹 Powered by Mayank, Wasim, Pravishank – Innovating Healthcare with AI! 💡 Your Health, Our Mission. 🚀") | |
| ''' | |
| import streamlit as st | |
| import speech_recognition as sr | |
| # import sounddevice as sd | |
| from deep_translator import GoogleTranslator | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import folium | |
| from streamlit_folium import folium_static | |
| from geopy.geocoders import Nominatim | |
| from geopy.distance import geodesic | |
| import requests | |
| # Set up AI model | |
| llm = ChatGoogleGenerativeAI( | |
| model="gemini-1.5-flash", | |
| google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE", | |
| temperature=0.5 | |
| ) | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .big-font { font-size:20px !important; } | |
| .stButton>button { background-color: #ff4b4b; color: white; font-size: 18px; } | |
| .stTextInput>div>div>input { font-size: 16px; } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # UI Setup | |
| st.title("🩺 CureBot: AI-Driven Health Assistant") | |
| st.write("Empowering healthcare with AI-driven insights and recommendations!") | |
| # Sidebar Navigation | |
| st.sidebar.title("🔍 Navigation") | |
| option = st.sidebar.radio("Select an option:", ["Home", "Symptom Checker", "Doctor Connect", "Health Stats", "Health Risk Calculator"]) | |
| translator = GoogleTranslator(source='auto', target='en') | |
| # Function to Get User Location | |
| def get_user_location(): | |
| try: | |
| response = requests.get("https://ipinfo.io/json").json() | |
| location = response["loc"].split(",") | |
| return float(location[0]), float(location[1]) | |
| except: | |
| return None, None | |
| if option == "Home": | |
| user_question = st.text_input("Type your disease name:") | |
| # st.title("🎤 AI Health Assistant - Speech to Text") | |
| # uploaded_file = st.file_uploader("Upload an audio file (.wav)", type=["wav"]) | |
| # if uploaded_file: | |
| # recognizer = sr.Recognizer() | |
| # with sr.AudioFile(uploaded_file) as source: | |
| # st.info("Processing audio...") | |
| # audio = recognizer.record(source) | |
| # try: | |
| # text = recognizer.recognize_google(audio) | |
| # st.success(f"Recognized Text: {text}") | |
| # except sr.UnknownValueError: | |
| # st.error("Could not understand the audio") | |
| # except sr.RequestError: | |
| # st.error("Error with the speech recognition service") | |
| # lang = st.selectbox("Select Language", ["English", "Hindi", "Spanish"]) | |
| # if lang != "English": | |
| # user_question = translator.translate(user_question, src="en", dest=lang.lower()).text | |
| if st.button("Get Recommendation"): | |
| if user_question.strip(): | |
| formatted_question = ( | |
| f"Provide medicine and alternative treatments for {user_question}. " | |
| f"List medicines, home remedies, lifestyle changes, exercises, and diet suggestions." | |
| ) | |
| with st.spinner("Analyzing..."): | |
| response = llm.invoke(formatted_question) | |
| response_text = response.content if hasattr(response, "content") else str(response) | |
| st.success("✨ Analysis complete! Here are your recommendations:") | |
| st.markdown(response_text) | |
| else: | |
| st.warning("Please enter a symptom or disease name!") | |
| elif option == "Symptom Checker": | |
| st.subheader("🔎 AI Symptom Checker") | |
| st.write("Find possible diseases based on symptoms.") | |
| symptoms = st.text_area("Enter symptoms separated by commas:") | |
| if st.button("Check Symptoms"): | |
| symptom_query = f"Analyze these symptoms: {symptoms}. List possible diseases." | |
| response = llm.invoke(symptom_query) | |
| st.write(response.content if hasattr(response, "content") else str(response)) | |
| elif option == "Symptom Checker": | |
| st.subheader("🔎 AI Symptom Checker") | |
| st.write("Find possible diseases based on symptoms.") | |
| symptoms = st.text_area("Enter symptoms separated by commas:") | |
| if st.button("Check Symptoms"): | |
| symptom_query = f"Analyze these symptoms: {symptoms}. List possible diseases." | |
| response = llm.invoke(symptom_query) | |
| st.write(response.content if hasattr(response, "content") else str(response)) | |
| elif option == "Doctor Connect": | |
| st.subheader("🏥 Find a Doctor Near You") | |
| address = st.text_input("Enter your location (City, State or Latitude, Longitude):") | |
| if address: | |
| geolocator = Nominatim(user_agent="geoapi") | |
| location = geolocator.geocode(address) | |
| if location: | |
| lat, lon = location.latitude, location.longitude | |
| st.success(f"📍 Location detected: {location.address} ({lat}, {lon})") | |
| map_ = folium.Map(location=[lat, lon], zoom_start=13) | |
| folium.Marker([lat, lon], popup="You are here!", icon=folium.Icon(color="blue")).add_to(map_) | |
| folium_static(map_) | |
| else: | |
| st.error("❌ Invalid address! Please try a different one.") | |
| else: | |
| st.info("ℹ️ Please enter a location to view it on the map.") | |
| # elif option == "Doctor Connect": | |
| # st.subheader("📍 Doctor Connect - Find Nearby Hospitals") | |
| # def get_user_location(): | |
| # geolocator = Nominatim(user_agent="geoapi") | |
| # location = geolocator.geocode("Phagwara") # Replace with dynamic location if needed | |
| # if location: | |
| # return location.latitude, location.longitude | |
| # return None | |
| # def find_nearby_hospitals(user_location): | |
| # hospitals = [ | |
| # {"name": "Apollo Hospital", "location": (30.7333, 76.7794)}, | |
| # {"name": "Fortis Hospital", "location": (30.7194, 76.7644)}, | |
| # {"name": "Max Hospital", "location": (30.7086, 76.7853)}, | |
| # {"name": "AIIMS Hospital", "location": (30.7500, 76.7800)} | |
| # ] | |
| # nearby_hospitals = [] | |
| # for hospital in hospitals: | |
| # distance = geodesic(user_location, hospital["location"]).km | |
| # if distance <= 10: | |
| # nearby_hospitals.append(hospital) | |
| # return nearby_hospitals | |
| # user_location = get_user_location() | |
| # if user_location: | |
| # st.success(f"📍 Your Location: {user_location}") | |
| # m = folium.Map(location=user_location, zoom_start=13) | |
| # folium.Marker(user_location, tooltip="Your Location", icon=folium.Icon(color="blue")).add_to(m) | |
| # hospitals = find_nearby_hospitals(user_location) | |
| # if hospitals: | |
| # st.write("🏥 Nearby Hospitals:") | |
| # for hospital in hospitals: | |
| # st.write(f"🔹 {hospital['name']}") | |
| # folium.Marker(hospital["location"], tooltip=hospital["name"], icon=folium.Icon(color="red")).add_to(m) | |
| # else: | |
| # st.write("⚠️ No nearby hospitals found within 10 km.") | |
| # folium_static(m) | |
| # else: | |
| # st.error("⚠️ Unable to fetch location. Please allow location access or enter manually.") | |
| elif option == "Health Stats": | |
| st.subheader("📊 Health Trends & Data") | |
| diseases = ['Diabetes', 'Hypertension', 'Heart Disease', 'Asthma', 'Obesity'] | |
| cases = np.random.randint(5000, 20000, size=len(diseases)) | |
| fig, ax = plt.subplots() | |
| ax.barh(diseases, cases, color=['blue', 'green', 'red', 'purple', 'orange']) | |
| ax.set_xlabel("Number of Cases") | |
| ax.set_title("Disease Prevalence Statistics") | |
| st.pyplot(fig) | |
| elif option == "Health Risk Calculator": | |
| st.subheader("🧮 Health Risk Calculator") | |
| tabs = st.tabs(["🏋️ BMI Calculator", "❤️ Heart Risk Estimator", "🩸 Diabetes Risk Score"]) | |
| # --- BMI CALCULATOR --- | |
| with tabs[0]: | |
| st.markdown("### 🏋️ Body Mass Index (BMI) Calculator") | |
| height_cm = st.number_input("Enter height (in cm):", min_value=50.0, max_value=250.0, step=0.1) | |
| weight_kg = st.number_input("Enter weight (in kg):", min_value=10.0, max_value=300.0, step=0.1) | |
| if st.button("Calculate BMI"): | |
| if height_cm > 0: | |
| height_m = height_cm / 100 | |
| bmi = weight_kg / (height_m ** 2) | |
| st.success(f"Your BMI is: **{bmi:.2f}**") | |
| if bmi < 18.5: | |
| st.info("Underweight") | |
| elif 18.5 <= bmi < 24.9: | |
| st.success("Normal weight") | |
| elif 25 <= bmi < 29.9: | |
| st.warning("Overweight") | |
| else: | |
| st.error("Obese") | |
| else: | |
| st.warning("Height must be greater than 0.") | |
| # --- HEART ATTACK RISK --- | |
| with tabs[1]: | |
| st.markdown("### ❤️ Heart Attack Risk Estimator") | |
| age = st.slider("Age", 18, 100, 30) | |
| gender = st.radio("Gender", ["Male", "Female"]) | |
| smoker = st.radio("Do you smoke?", ["Yes", "No"]) | |
| systolic_bp = st.slider("Systolic Blood Pressure (mmHg)", 80, 200, 120) | |
| cholesterol = st.slider("Cholesterol Level (mg/dL)", 100, 400, 200) | |
| if st.button("Estimate Heart Attack Risk"): | |
| risk_score = 0 | |
| if age > 45: risk_score += 1 | |
| if smoker == "Yes": risk_score += 1 | |
| if systolic_bp > 140: risk_score += 1 | |
| if cholesterol > 240: risk_score += 1 | |
| if gender == "Male": risk_score += 0.5 | |
| if risk_score <= 1: | |
| st.success("Low Risk ✅") | |
| elif 2 <= risk_score <= 3: | |
| st.warning("Moderate Risk ⚠️") | |
| else: | |
| st.error("High Risk ❌") | |
| # --- DIABETES RISK --- | |
| with tabs[2]: | |
| st.markdown("### 🩸 Diabetes Risk Score Estimator") | |
| age_d = st.slider("Age", 10, 100, 30, key="d1") | |
| bmi_d = st.slider("BMI", 10.0, 50.0, 22.0, step=0.1, key="d2") | |
| family_history = st.radio("Family History of Diabetes?", ["Yes", "No"], key="d3") | |
| physical_activity = st.radio("Do you exercise regularly?", ["Yes", "No"], key="d4") | |
| diet = st.radio("Do you consume sugary or high-carb food often?", ["Yes", "No"], key="d5") | |
| if st.button("Estimate Diabetes Risk"): | |
| d_score = 0 | |
| if age_d > 45: d_score += 1 | |
| if bmi_d > 30: d_score += 1 | |
| if family_history == "Yes": d_score += 1 | |
| if physical_activity == "No": d_score += 1 | |
| if diet == "Yes": d_score += 1 | |
| if d_score <= 1: | |
| st.success("Low Risk ✅") | |
| elif 2 <= d_score <= 3: | |
| st.warning("Moderate Risk ⚠️") | |
| else: | |
| st.error("High Risk ❌") | |
| # Emergency Contact Section | |
| st.sidebar.markdown("---") | |
| st.sidebar.subheader("📞 Emergency Contacts") | |
| st.sidebar.write("- 🚑 *Ambulance:* 102") | |
| st.sidebar.write("- 🏥 *LPU Hospital:* 18001024432") | |
| st.sidebar.write("- 🏥 *National Health Helpline:* 108") | |
| st.sidebar.write("- ☎ *COVID-19 Helpline:* 1075") | |
| st.sidebar.write("- 🚓 *Police:* 100") | |
| st.markdown("---") | |
| st.markdown("🔹 Powered by Mayank, Wasim, Pravisank, Ananya – Innovating Healthcare with AI! 💡 Your Health, Our Mission. 🚀") | |
| # ''' | |
| # import streamlit as st | |
| # import speech_recognition as sr | |
| # from deep_translator import GoogleTranslator | |
| # from langchain_google_genai import ChatGoogleGenerativeAI | |
| # import matplotlib.pyplot as plt | |
| # import numpy as np | |
| # import folium | |
| # from streamlit_folium import folium_static | |
| # import requests | |
| # from geopy.geocoders import Nominatim | |
| # # Initialize AI Model | |
| # llm = ChatGoogleGenerativeAI( | |
| # model="gemini-1.5-flash", | |
| # google_api_key="YOUR_GOOGLE_API_KEY", | |
| # temperature=0.5 | |
| # ) | |
| # # Custom CSS | |
| # st.markdown(""" | |
| # <style> | |
| # .big-font { font-size:20px !important; } | |
| # .stButton>button { background-color: #ff4b4b; color: white; font-size: 18px; } | |
| # .stTextInput>div>div>input { font-size: 16px; } | |
| # </style> | |
| # """, unsafe_allow_html=True) | |
| # # App Title & Description | |
| # st.title("🩺 CureBot: AI-Driven Health Assistant") | |
| # st.write("Empowering healthcare with AI-driven insights and recommendations!") | |
| # # Sidebar Navigation | |
| # st.sidebar.title("🔍 Navigation") | |
| # option = st.sidebar.radio("Select an option:", ["Home", "Symptom Checker", "Doctor Connect", "Health Stats"]) | |
| # translator = GoogleTranslator(source='auto', target='en') | |
| # Function to Get User Location | |
| # def get_user_location(): | |
| # try: | |
| # response = requests.get("https://ipinfo.io/json").json() | |
| # location = response["loc"].split(",") | |
| # return float(location[0]), float(location[1]) | |
| # except: | |
| # return None, None | |
| # if option == "Home": | |
| # user_question = st.text_input("Type your symptoms or disease name:") | |
| # if st.button("🎤 Speak Symptoms"): | |
| # recognizer = sr.Recognizer() | |
| # with sr.Microphone() as source: | |
| # st.info("Listening...") | |
| # try: | |
| # audio = recognizer.listen(source) | |
| # user_question = recognizer.recognize_google(audio) | |
| # st.success(f"Recognized: {user_question}") | |
| # except sr.UnknownValueError: | |
| # st.error("Could not understand audio") | |
| # except sr.RequestError: | |
| # st.error("Error in speech recognition service") | |
| # lang = st.selectbox("Select Language", ["English", "Hindi", "Spanish"]) | |
| # if lang != "English": | |
| # user_question = translator.translate(user_question, src="en", dest=lang.lower()) | |
| # if st.button("Get Recommendation"): | |
| # if user_question.strip(): | |
| # formatted_question = ( | |
| # f"Provide medicine and alternative treatments for {user_question}. " | |
| # f"List medicines, home remedies, lifestyle changes, exercises, and diet suggestions." | |
| # ) | |
| # with st.spinner("Analyzing..."): | |
| # response = llm.invoke(formatted_question) | |
| # response_text = response.content if hasattr(response, "content") else str(response) | |
| # st.success("✨ Analysis complete! Here are your recommendations:") | |
| # st.markdown(response_text) | |
| # else: | |
| # st.warning("Please enter a symptom or disease name!") | |
| # elif option == "Symptom Checker": | |
| # st.subheader("🔎 AI Symptom Checker") | |
| # symptoms = st.text_area("Enter symptoms separated by commas:") | |
| # if st.button("Check Symptoms"): | |
| # symptom_query = f"Analyze these symptoms: {symptoms}. List possible diseases." | |
| # response = llm.invoke(symptom_query) | |
| # st.write(response.content if hasattr(response, "content") else str(response)) | |
| # elif option == "Doctor Connect": | |
| # st.subheader("🏥 Find a Doctor Near You") | |
| # lat, lon = get_user_location() | |
| # if lat is None or lon is None: | |
| # st.warning("Unable to fetch location. Please allow location access or enter manually.") | |
| # address = st.text_input("Enter your location (City, State or Latitude, Longitude):") | |
| # if address: | |
| # geolocator = Nominatim(user_agent="geoapi") | |
| # location = geolocator.geocode(address) | |
| # if location: | |
| # lat, lon = location.latitude, location.longitude | |
| # else: | |
| # st.error("Invalid address! Try again.") | |
| # if lat and lon: | |
| # st.success(f"📍 Location detected: {lat}, {lon}") | |
| # map_ = folium.Map(location=[lat, lon], zoom_start=13) | |
| # folium.Marker([lat, lon], popup="You are here!", icon=folium.Icon(color="blue")).add_to(map_) | |
| # folium_static(map_) | |
| # elif option == "Health Stats": | |
| # st.subheader("📊 Health Trends & Data") | |
| # diseases = ['Diabetes', 'Hypertension', 'Heart Disease', 'Asthma', 'Obesity'] | |
| # cases = np.random.randint(5000, 20000, size=len(diseases)) | |
| # fig, ax = plt.subplots() | |
| # ax.barh(diseases, cases, color=['blue', 'green', 'red', 'purple', 'orange']) | |
| # ax.set_xlabel("Number of Cases") | |
| # ax.set_title("Disease Prevalence Statistics") | |
| # st.pyplot(fig) | |
| # # Emergency Contacts | |
| # st.sidebar.markdown("---") | |
| # st.sidebar.subheader("📞 Emergency Contacts") | |
| # st.sidebar.write("- 🚑 *Ambulance:* 102") | |
| # st.sidebar.write("- 🏥 *LPU Hospital:* 18001024432") | |
| # st.sidebar.write("- 🏥 *National Health Helpline:* 108") | |
| # st.sidebar.write("- ☎ *COVID-19 Helpline:* 1075") | |
| # st.sidebar.write("- 🚓 *Police:* 1000") | |
| # st.markdown("---") | |
| # st.markdown("🔹 Powered by Mayank, Wasim, Pravishank – Innovating Healthcare with AI! 💡 Your Health, Our Mission. 🚀") | |
| # ''' |