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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, response | |
| # 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() | |
| # Emotion detection function with suggestions in plain English and resources in table | |
| def detect_emotion(user_input): | |
| pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion) | |
| result = pipe(user_input) | |
| emotion = result[0]['label'] | |
| # Define emotion-specific message and resources | |
| if emotion == 'joy': | |
| emotion_msg = "You're feeling happy! Keep up the great mood!" | |
| resources = [ | |
| {"subject": "Relaxation Techniques", "heading": "Mindful Breathing Meditation", "link": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation", "video_url": "https://youtu.be/m1vaUGtyo-A"}, | |
| {"subject": "Dealing with Stress", "heading": "Tips for Dealing with Anxiety", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety", "video_url": "https://youtu.be/m1vaUGtyo-A"}, | |
| {"subject": "Emotional Wellness Toolkit", "heading": "Emotional Wellness Resources", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit", "video_url": "https://youtu.be/m1vaUGtyo-A"} | |
| ] | |
| elif emotion == 'anger': | |
| emotion_msg = "You're feeling angry. It's okay to feel this way. Let's try to calm down." | |
| resources = [ | |
| {"subject": "Emotional Wellness Toolkit", "heading": "Managing Emotions", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit", "video_url": "https://youtu.be/MIc299Flibs"}, | |
| {"subject": "Stress Management Tips", "heading": "Managing Stress Effectively", "link": "https://www.health.harvard.edu/health-a-to-z", "video_url": "https://youtu.be/MIc299Flibs"}, | |
| {"subject": "Dealing with Anger", "heading": "Strategies to Calm Anger", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety", "video_url": "https://youtu.be/MIc299Flibs"} | |
| ] | |
| elif emotion == 'fear': | |
| emotion_msg = "You're feeling fearful. Take a moment to breathe and relax." | |
| resources = [ | |
| {"subject": "Mindfulness Practices", "heading": "Breathing Techniques", "link": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation", "video_url": "https://youtu.be/yGKKz185M5o"}, | |
| {"subject": "Coping with Anxiety", "heading": "Overcoming Fear", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety", "video_url": "https://youtu.be/yGKKz185M5o"}, | |
| {"subject": "Emotional Wellness Toolkit", "heading": "Calming Your Mind", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit", "video_url": "https://youtu.be/yGKKz185M5o"} | |
| ] | |
| elif emotion == 'sadness': | |
| emotion_msg = "You're feeling sad. It's okay to take a break." | |
| resources = [ | |
| {"subject": "Emotional Wellness Toolkit", "heading": "Restoring Your Emotional Health", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit", "video_url": "https://youtu.be/-e-4Kx5px_I"}, | |
| {"subject": "Dealing with Anxiety", "heading": "Coping Strategies for Stress", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety", "video_url": "https://youtu.be/-e-4Kx5px_I"} | |
| ] | |
| elif emotion == 'surprise': | |
| emotion_msg = "You're feeling surprised. It's okay to feel neutral!" | |
| resources = [ | |
| {"subject": "Managing Stress", "heading": "Relaxation Tips", "link": "https://www.health.harvard.edu/health-a-to-z", "video_url": "https://youtu.be/m1vaUGtyo-A"}, | |
| {"subject": "Coping Strategies", "heading": "Dealing with Unexpected Events", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety", "video_url": "https://youtu.be/m1vaUGtyo-A"} | |
| ] | |
| else: | |
| emotion_msg = "Could not detect emotion." | |
| resources = [] | |
| # Create a DataFrame for resources to display in table format | |
| resource_df = pd.DataFrame(resources) | |
| return emotion_msg, resource_df | |
| # Google Geocoding API setup to convert city name to latitude/longitude | |
| geocode_url = "https://maps.googleapis.com/maps/api/geocode/json" | |
| def get_lat_lon(location, api_key): | |
| params = { | |
| "address": location, | |
| "key": api_key | |
| } | |
| response = requests.get(geocode_url, params=params) | |
| if response.status_code == 200: | |
| result = response.json() | |
| if result['status'] == 'OK': | |
| # Return the first result's latitude and longitude | |
| location = result['results'][0]['geometry']['location'] | |
| return location['lat'], location['lng'] | |
| return None, None | |
| # 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) | |
| return response.json() | |
| # Function to fetch wellness professionals | |
| def get_wellness_professionals(location, api_key): | |
| lat, lon = get_lat_lon(location, api_key) | |
| if lat and lon: | |
| places = get_places_data("wellness professional", f"{lat},{lon}", 10000, api_key) | |
| if places and 'results' in places: | |
| professionals = [] | |
| for place in places['results']: | |
| name = place.get("name", "No name available") | |
| rating = place.get("rating", "No rating available") | |
| address = place.get("formatted_address", "No address available") | |
| professionals.append({ | |
| "Name": name, | |
| "Rating": rating, | |
| "Address": address | |
| }) | |
| professionals_df = pd.DataFrame(professionals) | |
| return professionals_df | |
| else: | |
| return "No wellness professionals found nearby." | |
| else: | |
| return "Location not found. Please check the location." | |
| # Gradio interface function to handle actions and outputs | |
| def interface_function(message, action, location, history): | |
| history = history or [] | |
| if action == "Chat": | |
| # Use chat function if 'Chat' button is clicked | |
| history, response = chat(message, history) | |
| elif action == "Detect Emotion": | |
| # Use emotion detection if 'Detect Emotion' button is clicked | |
| emotion_msg, resource_df = detect_emotion(message) | |
| response = emotion_msg | |
| # Return the resource DataFrame as a table | |
| return history, response, resource_df | |
| elif action == "Wellness Resources": | |
| # Use location to get wellness professionals if 'Wellness Resources' is clicked | |
| if not location.strip(): | |
| response = "Please enter a valid location." | |
| else: | |
| professionals_df = get_wellness_professionals(location, api_key) | |
| if isinstance(professionals_df, pd.DataFrame): | |
| response = "Found wellness professionals nearby:" | |
| return history, response, professionals_df | |
| else: | |
| response = professionals_df # If error message is returned | |
| return history, response, None | |
| return history, "Invalid action", None | |
| # Gradio Interface with table outputs for emotion and wellness professionals | |
| iface = gr.Interface( | |
| fn=interface_function, | |
| inputs=["text", "radio", "text", "state"], # Include state in the inputs | |
| outputs=["text", "dataframe", "state"], # Add state to the outputs | |
| live=True, | |
| allow_flagging="never" | |
| ) | |
| iface.launch(share=True) | |