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| import json | |
| import pickle | |
| import random | |
| import nltk | |
| import numpy as np | |
| import tflearn | |
| import gradio as gr | |
| import requests | |
| import torch | |
| import pandas as pd | |
| from bs4 import BeautifulSoup | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| from nltk.tokenize import word_tokenize | |
| from nltk.stem.lancaster import LancasterStemmer | |
| import os | |
| # Ensure necessary NLTK resources are downloaded | |
| nltk.download('punkt') | |
| # Initialize the stemmer | |
| stemmer = LancasterStemmer() | |
| # Load intents.json | |
| try: | |
| with open("intents.json") as file: | |
| data = json.load(file) | |
| except FileNotFoundError: | |
| raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.") | |
| # Load preprocessed data from pickle | |
| try: | |
| with open("data.pickle", "rb") as f: | |
| words, labels, training, output = pickle.load(f) | |
| except FileNotFoundError: | |
| raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.") | |
| # Build the model structure | |
| 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) | |
| # Load the trained model | |
| model = tflearn.DNN(net) | |
| try: | |
| model.load("MentalHealthChatBotmodel.tflearn") | |
| except FileNotFoundError: | |
| raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found.") | |
| # Function to process user input into a bag-of-words format | |
| def bag_of_words(s, words): | |
| bag = [0 for _ in range(len(words))] | |
| s_words = word_tokenize(s) | |
| s_words = [stemmer.stem(word.lower()) for word in s_words if word.lower() in words] | |
| for se in s_words: | |
| for i, w in enumerate(words): | |
| if w == se: | |
| bag[i] = 1 | |
| return np.array(bag) | |
| # Chat function | |
| def chat(message, history): | |
| history = history or [] | |
| message = message.lower() | |
| try: | |
| # Predict the tag | |
| results = model.predict([bag_of_words(message, words)]) | |
| results_index = np.argmax(results) | |
| tag = labels[results_index] | |
| # Match tag with intent and choose a random response | |
| for tg in data["intents"]: | |
| if tg['tag'] == tag: | |
| responses = tg['responses'] | |
| response = random.choice(responses) | |
| break | |
| else: | |
| response = "I'm sorry, I didn't understand that. Could you please rephrase?" | |
| except Exception as e: | |
| response = f"An error occurred: {str(e)}" | |
| history.append((message, response)) | |
| return history, history | |
| # Sentiment analysis setup | |
| tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") | |
| model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") | |
| # Emotion detection setup | |
| def load_emotion_model(): | |
| tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base") | |
| model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base") | |
| return tokenizer, model | |
| tokenizer_emotion, model_emotion = load_emotion_model() | |
| # Google Places API setup for wellness professionals | |
| url = "https://maps.googleapis.com/maps/api/place/textsearch/json" | |
| places_details_url = "https://maps.googleapis.com/maps/api/place/details/json" | |
| api_key = os.getenv("GOOGLE_API_KEY") # Use environment variable for security | |
| # Function to get places data using Google Places API | |
| def get_places_data(query, location, radius, api_key, next_page_token=None): | |
| params = { | |
| "query": query, | |
| "location": location, | |
| "radius": radius, | |
| "key": api_key | |
| } | |
| if next_page_token: | |
| params["pagetoken"] = next_page_token | |
| response = requests.get(url, params=params) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return None | |
| # Web scraping function to get wellness professional data (alternative to API) | |
| def scrape_wellness_professionals(query, location): | |
| # User-Agent header to simulate a browser request | |
| headers = { | |
| "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" | |
| } | |
| search_url = f"https://www.google.com/search?q={query}+near+{location}" | |
| # Make a request to the search URL with headers | |
| response = requests.get(search_url, headers=headers) | |
| if response.status_code == 200: | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| # Parse and extract wellness professionals from the HTML | |
| wellness_data = [] | |
| results = soup.find_all("div", class_="BVG0Nb") # Adjust class based on the actual HTML structure | |
| for result in results: | |
| name = result.get_text() | |
| link = result.find("a")["href"] if result.find("a") else None | |
| wellness_data.append([name, link]) | |
| return wellness_data | |
| else: | |
| return [] | |
| # Main function to fetch wellness professional data and display on map | |
| def get_wellness_professionals(location): | |
| query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist OR nutritionist OR wellness doctor OR holistic practitioner OR integrative medicine OR chiropractor OR naturopath" | |
| radius = 50000 # 50 km radius | |
| # Using Google Places API if available | |
| data = get_places_data(query, location, radius, api_key) | |
| if data: | |
| results = data.get('results', []) | |
| wellness_data = [] | |
| for place in results: | |
| name = place.get("name") | |
| address = place.get("formatted_address") | |
| latitude = place.get("geometry", {}).get("location", {}).get("lat") | |
| longitude = place.get("geometry", {}).get("location", {}).get("lng") | |
| wellness_data.append([name, address, latitude, longitude]) | |
| return wellness_data | |
| # Fallback to scraping if API is not available or fails | |
| return scrape_wellness_professionals(query, location) | |
| # Emotion detection function with suggestions | |
| def detect_emotion(user_input): | |
| pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion) | |
| result = pipe(user_input) | |
| emotion = result[0]['label'] | |
| # Provide suggestions based on the detected emotion | |
| if emotion == 'joy': | |
| return ("You're feeling happy! Keep up the great mood!", | |
| [("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")], | |
| "Watch on YouTube: https://youtu.be/m1vaUGtyo-A") | |
| elif emotion == 'anger': | |
| return ("You're feeling angry. It's okay to feel this way. Let's try to calm down.", | |
| [("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")], | |
| "Watch on YouTube: https://youtu.be/MIc299Flibs") | |
| elif emotion == 'fear': | |
| return ("You're feeling fearful. Take a moment to breathe and relax.", | |
| [("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")], | |
| "Watch on YouTube: https://youtu.be/yGKKz185M5o") | |
| elif emotion == 'sadness': | |
| return ("You're feeling sad. It's okay to take a break.", | |
| [("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")], | |
| "Watch on YouTube: https://youtu.be/-e-4Kx5px_I") | |
| elif emotion == 'surprise': | |
| return ("You're feeling surprised. It's okay to feel neutral!", | |
| [("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")], | |
| "Watch on YouTube: https://youtu.be/m1vaUGtyo-A") | |
| return ("Could not detect emotion.", [], "") | |
| # Gradio interface setup for user interaction | |
| def user_interface(message, location, history): | |
| history, history = chat(message, history) | |
| # Sentiment analysis | |
| inputs = tokenizer_sentiment(message, return_tensors="pt") | |
| outputs = model_sentiment(**inputs) | |
| sentiment = ["Negative", "Neutral", "Positive"][torch.argmax(outputs.logits, dim=1).item()] | |
| # Emotion detection | |
| emotion_msg, resources, video_link = detect_emotion(message) | |
| # Get wellness professionals | |
| wellness_data = get_wellness_professionals(location) | |
| # Display wellness professionals in a table format | |
| wellness_df = pd.DataFrame(wellness_data, columns=["Name", "Address", "Latitude", "Longitude"]) | |
| return history, history, sentiment, emotion_msg, resources, video_link, wellness_df.to_html(escape=False) | |
| # Gradio chatbot interface | |
| chatbot = gr.Chatbot(label="Mental Health Chatbot") | |
| location_input = gr.Textbox(label="Enter your location (latitude,longitude)", placeholder="e.g., 21.3,-157.8") | |
| # Gradio interface definition | |
| demo = gr.Interface( | |
| user_interface, | |
| [gr.Textbox(label="Message"), location_input, "state"], | |
| [chatbot, "state", "text", "text", "json", "text", "html"], | |
| allow_flagging="never", | |
| title="Mental Health & Well-being Assistant" | |
| ) | |
| # Launch Gradio interface | |
| if __name__ == "__main__": | |
| demo.launch() | |