import os import gradio as gr 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 # 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): 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): 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): 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): 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): emotion_key = emotion.lower() suggestions = { "joy": [ ["Relaxation Techniques", 'Visit'], ["Emotional Toolkit", 'Visit'], ["Stress Management", 'Visit'], ], "anger": [ ["Handle Anger", 'Watch'], ["Stress Tips", 'Visit'], ], "fear": [ ["Coping with Anxiety", 'Visit'], ["Mindfulness", 'Watch'], ], "sadness": [ ["Overcoming Sadness", 'Watch'], ], "surprise": [ ["Managing Surprises", 'Visit'], ["Relaxation Video", 'Watch'], ], "neutral": [ ["General Well-Being Tips", 'Visit'], ], } return suggestions.get(emotion_key, [["No specific suggestions available.", ""]]) def get_health_professionals_and_map(location, query): try: if not location or not query: return ["Please provide both location and query."], "" 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: professionals.append(f"{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 ["No professionals found for the given location."], "" except Exception as e: return [f"An error occurred: {e}"], "" # Main Application Logic def app_function(user_input, location, query, history): chatbot_history, _ = generate_chatbot_response(user_input, history) 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) return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html # Gradio Interface custom_css = """ body { font-family: 'Roboto', sans-serif; background: linear-gradient(135deg,#0d0d0d,#ff5722); color: white; } h1 { background: #ffffff; color: #000000; border-radius: 8px; padding: 10px; font-weight: bold; text-align: center; font-size: 2.5rem; } textarea, input { background: black; /* Maintains black background */ color: white; /* Text color stays white */ border: 2px solid orange; font-size: 1rem; padding: 10px; border-radius: 8px; caret-color: white; /* Ensures white text cursor */ } /* Enable black background and white text even on focus/typing */ textarea:focus, input:focus { background: black; /* Black background when focused */ color: white; /* White text remains consistent */ border: 2px solid orange; /* Border is orange on focus for emphasis */ outline: none; /* Removes default browser outline */ } button { background: linear-gradient(135deg, orange, #ff4500); color: white; padding: 10px; border-radius: 8px; font-weight: bold; font-size: 1.2rem; border: none; cursor: pointer; } button:hover { box-shadow: 0px 4px 8px rgba(255, 165, 0, 0.5); } """ with gr.Blocks(css=custom_css) as app: gr.HTML("

🌟 Well-Being Companion

") with gr.Row(): user_input = gr.Textbox(label="Your Message") location = gr.Textbox(label="Your Location") query = gr.Textbox(label="Search Query") chatbot = gr.Chatbot(label="Chat History") sentiment = gr.Textbox(label="Detected Sentiment") emotion = gr.Textbox(label="Detected Emotion") suggestions = gr.DataFrame(headers=["Title", "Link"]) professionals = gr.Textbox(label="Nearby Professionals", lines=6) map_html = gr.HTML(label="Interactive Map") submit = gr.Button("Submit") submit.click( app_function, inputs=[user_input, location, query, chatbot], outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html] ) app.launch()