Spaces:
Sleeping
Sleeping
| 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() | |
| # Emotion detection function with suggestions in plain English | |
| 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': | |
| emotion_msg = "You're feeling happy! Keep up the great mood!" | |
| resources = [ | |
| {"subject": "Relaxation Techniques", "link": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"}, | |
| {"subject": "Dealing with Stress", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"}, | |
| {"subject": "Emotional Wellness Toolkit", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"} | |
| ] | |
| video_link = "Watch on YouTube: 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", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"}, | |
| {"subject": "Stress Management Tips", "link": "https://www.health.harvard.edu/health-a-to-z"}, | |
| {"subject": "Dealing with Anger", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"} | |
| ] | |
| video_link = "Watch on YouTube: https://youtu.be/MIc299Flibs" | |
| elif emotion == 'fear': | |
| emotion_msg = "You're feeling fearful. Take a moment to breathe and relax." | |
| resources = [ | |
| {"subject": "Mindfulness Practices", "link": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"}, | |
| {"subject": "Coping with Anxiety", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"}, | |
| {"subject": "Emotional Wellness Toolkit", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"} | |
| ] | |
| video_link = "Watch on YouTube: 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", "link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"}, | |
| {"subject": "Dealing with Anxiety", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"} | |
| ] | |
| video_link = "Watch on YouTube: 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", "link": "https://www.health.harvard.edu/health-a-to-z"}, | |
| {"subject": "Coping Strategies", "link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"} | |
| ] | |
| video_link = "Watch on YouTube: https://youtu.be/m1vaUGtyo-A" | |
| else: | |
| emotion_msg = "Could not detect emotion." | |
| resources = [] | |
| video_link = "" | |
| return emotion_msg, resources, video_link | |
| # Create the interface with multiple buttons | |
| def interface_function(message, action, history): | |
| history = history or [] | |
| if action == "Chat": | |
| # Use chat function if 'Chat' button is clicked | |
| history, _ = chat(message, history) | |
| return history, history | |
| elif action == "Detect Emotion": | |
| # Detect emotion if 'Detect Emotion' button is clicked | |
| emotion_msg, resources, video_link = detect_emotion(message) | |
| result = f"Emotion: {emotion_msg}\nResources:\n" | |
| for res in resources: | |
| result += f"- {res['subject']}: {res['link']}\n" | |
| result += f"Suggested Video: {video_link}" | |
| return result, history | |
| elif action == "Wellness Resources": | |
| # Return wellness resources if 'Wellness Resources' button is clicked | |
| result = "Here are some wellness resources you can explore:\n" | |
| result += "1. Mental Health Support: https://www.helpguide.org/mental-health/mental-health-support.htm\n" | |
| result += "2. Meditation Guide: https://www.headspace.com/meditation\n" | |
| return result, history | |
| # Gradio interface with multiple buttons | |
| iface = gr.Interface( | |
| fn=interface_function, | |
| inputs=[gr.Textbox(label="Enter message"), | |
| gr.Radio(["Chat", "Detect Emotion", "Wellness Resources"], label="Choose Action"), | |
| gr.State()], | |
| outputs=[gr.Textbox(label="Response"), gr.State()], | |
| allow_flagging="never", | |
| live=True | |
| ) | |
| iface.launch(share=True) | |