import os import nltk import numpy as np import tflearn import random import json import pickle from nltk.tokenize import word_tokenize from nltk.stem.lancaster import LancasterStemmer from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline import googlemaps import folium import torch import streamlit as st # Suppress TensorFlow warnings os.environ["CUDA_VISIBLE_DEVICES"] = "-1" os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # Download necessary NLTK resources nltk.download("punkt") stemmer = LancasterStemmer() # Load intents and chatbot training data with open("intents.json") as file: intents_data = json.load(file) with open("data.pickle", "rb") as f: words, labels, training, output = pickle.load(f) # Build the chatbot model net = tflearn.input_data(shape=[None, len(training[0])]) net = tflearn.fully_connected(net, 8) net = tflearn.fully_connected(net, 8) net = tflearn.fully_connected(net, len(output[0]), activation="softmax") net = tflearn.regression(net) chatbot_model = tflearn.DNN(net) chatbot_model.load("MentalHealthChatBotmodel.tflearn") # Hugging Face sentiment and emotion models tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base") model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base") # Google Maps API Client gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY")) # Helper Functions def bag_of_words(s, words): """Convert user input to bag-of-words vector.""" bag = [0] * len(words) s_words = word_tokenize(s) s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()] for se in s_words: for i, w in enumerate(words): if w == se: bag[i] = 1 return np.array(bag) def generate_chatbot_response(message, history): """Generate chatbot response and maintain conversation history.""" history = history or [] try: result = chatbot_model.predict([bag_of_words(message, words)]) tag = labels[np.argmax(result)] response = "I'm sorry, I didn't understand that. 🤔" for intent in intents_data["intents"]: if intent["tag"] == tag: response = random.choice(intent["responses"]) break except Exception as e: response = f"Error: {e}" history.append((message, response)) return history, response def analyze_sentiment(user_input): """Analyze sentiment and map to emojis.""" inputs = tokenizer_sentiment(user_input, return_tensors="pt") with torch.no_grad(): outputs = model_sentiment(**inputs) sentiment_class = torch.argmax(outputs.logits, dim=1).item() sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"] return f"Sentiment: {sentiment_map[sentiment_class]}" def detect_emotion(user_input): """Detect emotions based on input.""" pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion) result = pipe(user_input) emotion = result[0]["label"].lower().strip() emotion_map = { "joy": "Joy 😊", "anger": "Anger 😠", "sadness": "Sadness 😢", "fear": "Fear 😨", "surprise": "Surprise 😲", "neutral": "Neutral 😐", } return emotion_map.get(emotion, "Unknown 🤔"), emotion def generate_suggestions(emotion): """Return relevant suggestions based on detected emotions.""" emotion_key = emotion.lower() suggestions = { "joy": [ ["Relaxation Techniques", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"], ["Dealing with Stress", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"], ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"], ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"], ], "anger": [ ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"], ["Stress Management Tips", "https://www.health.harvard.edu/health-a-to-z"], ["Dealing with Anger", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"], ["Relaxation Video", "https://youtu.be/MIc299Flibs"], ], "fear": [ ["Mindfulness Practices", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"], ["Coping with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"], ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"], ["Relaxation Video", "https://youtu.be/yGKKz185M5o"], ], "sadness": [ ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"], ["Dealing with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"], ["Relaxation Video", "https://youtu.be/-e-4Kx5px_I"], ], "surprise": [ ["Managing Stress", "https://www.health.harvard.edu/health-a-to-z"], ["Coping Strategies", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"], ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"], ], } # Format the output to include HTML anchor tags formatted_suggestions = [ [title, f'{link}'] for title, link in suggestions.get(emotion_key, [["No specific suggestions available.", "#"]]) ] return formatted_suggestions def get_health_professionals_and_map(location, query): """Search nearby healthcare professionals using Google Maps API.""" try: if not location or not query: return [], "" # Return empty list if inputs are missing geo_location = gmaps.geocode(location) if geo_location: lat, lng = geo_location[0]["geometry"]["location"].values() places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"] professionals = [] map_ = folium.Map(location=(lat, lng), zoom_start=13) for place in places_result: # Use a list of values to append each professional professionals.append([place['name'], place.get('vicinity', 'No address provided')]) folium.Marker( location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]], popup=f"{place['name']}" ).add_to(map_) return professionals, map_._repr_html_() return [], "" # Return empty list if no professionals found except Exception as e: return [], "" # Return empty list on exception # Streamlit App Layout st.title("🌟 Well-Being Companion") # Input fields user_input = st.text_input("Please Enter Your Message Here") location = st.text_input("Please Enter Your Current Location Here") query = st.text_input("Please Enter Which Health Professional You Want To Search Nearby") # Button to submit if st.button("Submit"): chatbot_history, _ = generate_chatbot_response(user_input, []) sentiment_result = analyze_sentiment(user_input) emotion_result, cleaned_emotion = detect_emotion(user_input) suggestions = generate_suggestions(cleaned_emotion) professionals, map_html = get_health_professionals_and_map(location, query) # Display chatbot conversation history st.subheader("Chat History") for message, response in chatbot_history: st.write(f"**You:** {message}") st.write(f"**Bot:** {response}") # Display sentiment st.subheader("Detected Sentiment") st.write(sentiment_result) # Display emotion st.subheader("Detected Emotion") st.write(emotion_result) # Display suggestions st.subheader("Suggestions") for suggestion, link in suggestions: st.write(f"[{suggestion}]({link})") # Display professionals st.subheader("Nearby Health Professionals") st.write(professionals) # Display map st.subheader("Interactive Map") st.components.v1.html(map_html, height=500)