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| 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 | |
| # Disable GPU usage for TensorFlow | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '-1' | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' | |
| # Download NLTK resources | |
| nltk.download("punkt") | |
| # Initialize Lancaster Stemmer | |
| stemmer = LancasterStemmer() | |
| # Load chatbot intents and 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 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") | |
| # Model for sentiment detection | |
| tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") | |
| model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") | |
| # Model for emotion detection | |
| 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')) | |
| # Chatbot logic | |
| 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 chatbot(message, history): | |
| """Generate chatbot response and append to history.""" | |
| history = history or [] | |
| try: | |
| results = chatbot_model.predict([bag_of_words(message, words)]) | |
| tag = labels[np.argmax(results)] | |
| response = "I'm not sure how to respond to that. 🤔" | |
| for intent in intents_data["intents"]: | |
| if intent["tag"] == tag: | |
| response = random.choice(intent["responses"]) | |
| break | |
| except Exception as e: | |
| response = f"Error: {str(e)} 💥" | |
| history.append((message, response)) | |
| return history, response | |
| # Sentiment analysis | |
| 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 sentiment_map[sentiment_class] | |
| # Emotion detection | |
| def detect_emotion(user_input): | |
| pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion) | |
| result = pipe(user_input) | |
| emotion = result[0]["label"] | |
| return emotion | |
| # Generate Suggestions | |
| def generate_suggestions(emotion): | |
| suggestions = { | |
| "joy": [ | |
| ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'], | |
| ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'], | |
| ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'], | |
| ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'], | |
| ], | |
| "anger": [ | |
| ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'], | |
| ["Stress Management Tips", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'], | |
| ["Dealing with Anger", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'], | |
| ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'], | |
| ], | |
| } | |
| return suggestions.get(emotion, [["No suggestions available", ""]]) | |
| # Get Nearby Professionals and Generate Map | |
| def get_health_professionals_and_map(location, query): | |
| try: | |
| 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"] | |
| map_ = folium.Map(location=(lat, lng), zoom_start=13) | |
| professionals = [] | |
| for place in places_result: | |
| professionals.append(f"{place['name']} - {place.get('vicinity', '')}") | |
| folium.Marker([place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]], | |
| popup=place["name"]).add_to(map_) | |
| return professionals, map_._repr_html_() | |
| return ["No professionals found"], "" | |
| except Exception as e: | |
| return [f"Error: {e}"], "" | |
| # App Main Function | |
| def app_function(message, location, query, history): | |
| chatbot_history, _ = chatbot(message, history) | |
| sentiment = analyze_sentiment(message) | |
| emotion = detect_emotion(message.lower()) | |
| suggestions = generate_suggestions(emotion) | |
| professionals, map_html = get_health_professionals_and_map(location, query) | |
| return chatbot_history, sentiment, emotion, suggestions, professionals, map_html | |
| # Enhanced CSS for Custom Title and Styling | |
| custom_css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap'); | |
| body { | |
| background: linear-gradient(135deg, #000000, #ff5722); | |
| color: white; | |
| font-family: 'Roboto', sans-serif; | |
| } | |
| button { | |
| background-color: #ff5722 !important; | |
| border: none !important; | |
| color: white !important; | |
| padding: 12px 20px; | |
| font-size: 16px; | |
| border-radius: 8px; | |
| cursor: pointer; | |
| } | |
| button:hover { | |
| background-color: #e64a19 !important; | |
| } | |
| textarea, input[type="text"], .gr-chatbot { | |
| background: #000000 !important; | |
| color: white !important; | |
| border: 2px solid #ff5722 !important; | |
| padding: 12px !important; | |
| border-radius: 8px !important; | |
| font-size: 14px; | |
| } | |
| .gr-dataframe, .gr-textbox { | |
| background: #000000 !important; | |
| color: white !important; | |
| border: 2px solid #ff5722 !important; | |
| border-radius: 8px !important; | |
| font-size: 14px; | |
| } | |
| .suggestions-title { | |
| font-size: 1.5rem !important; | |
| font-weight: bold; | |
| color: white; | |
| margin-top: 20px; | |
| } | |
| h1 { | |
| font-size: 4rem; | |
| font-weight: bold; | |
| margin-bottom: 10px; | |
| color: white; | |
| text-align: center; | |
| text-shadow: 2px 2px 8px rgba(0, 0, 0, 0.6); | |
| } | |
| h2 { | |
| font-weight: 400; | |
| font-size: 1.8rem; | |
| color: white; | |
| text-shadow: 2px 2px 5px rgba(0, 0, 0, 0.4); | |
| } | |
| .input-title, .output-title { | |
| font-size: 1.5rem; | |
| font-weight: bold; | |
| color: black; | |
| margin-bottom: 10px; | |
| } | |
| """ | |
| # Gradio Interface | |
| with gr.Blocks(css=custom_css) as app: | |
| gr.HTML("<h1>🌟 Well-Being Companion</h1>") | |
| gr.HTML("<h2>Empowering Your Well-Being Journey 💚</h2>") | |
| with gr.Row(): | |
| gr.Markdown("<div class='input-title'>Your Message</div>") | |
| user_message = gr.Textbox(label=None, placeholder="Enter your message...") | |
| gr.Markdown("<div class='input-title'>Your Location</div>") | |
| user_location = gr.Textbox(label=None, placeholder="Enter your location...") | |
| gr.Markdown("<div class='input-title'>Your Query</div>") | |
| search_query = gr.Textbox(label=None, placeholder="Search for professionals...") | |
| chatbot_box = gr.Chatbot(label="Chat History") | |
| gr.Markdown("<div class='output-title'>Detected Emotion</div>") | |
| emotion_output = gr.Textbox(label=None) | |
| gr.Markdown("<div class='output-title'>Detected Sentiment</div>") | |
| sentiment_output = gr.Textbox(label=None) | |
| gr.Markdown("<div class='suggestions-title'>Suggestions</div>") | |
| suggestions_output = gr.DataFrame(headers=["Title", "Links"], label=None) | |
| gr.Markdown("<h2 class='suggestions-title'>Health Professionals Nearby</h2>") | |
| map_output = gr.HTML(label=None) | |
| professional_display = gr.Textbox(label=None, lines=5) | |
| submit_btn = gr.Button("Submit") | |
| submit_btn.click( | |
| app_function, | |
| inputs=[user_message, user_location, search_query, chatbot_box], | |
| outputs=[ | |
| chatbot_box, sentiment_output, emotion_output, | |
| suggestions_output, professional_display, map_output, | |
| ], | |
| ) | |
| app.launch() |