| # # # # # 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="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) | |
| # 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:* 100") | |
| st.markdown("---") | |
| st.markdown("🔹 Powered by Mayank, Wasim, Pravishank – Innovating Healthcare with AI! 💡 Your Health, Our Mission. 🚀") | |