Create app.py

app.py

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+import nltk
+import numpy as np
+import tflearn
+import tensorflow
+import random
+import json
+import pickle
+import gradio as gr
+from nltk.tokenize import word_tokenize
+from nltk.stem.lancaster import LancasterStemmer
+import requests
+import csv
+import time
+import re
+from bs4 import BeautifulSoup
+import pandas as pd
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+import chromedriver_autoinstaller
+import os
+import logging
+
+# 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
+
+# Load the pre-trained model (cached for performance)
+def load_model():
+    return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')
+
+sentiment_model = load_model()
+
+# Define the function to analyze sentiment
+def analyze_sentiment(user_input):
+    result = sentiment_model(user_input)[0]
+    sentiment = result['label'].lower()  # Convert to lowercase for easier comparison
+    
+    # Customize messages based on detected sentiment
+    if sentiment == 'negative':
+        return "Mood Detected: Negative 😔\n\nStay positive! 🌟 Remember, tough times don't last, but tough people do!"
+    elif sentiment == 'neutral':
+        return "Mood Detected: Neutral 😐\n\nIt's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
+    elif sentiment == 'positive':
+        return "Mood Detected: Positive 😊\n\nYou're on the right track! Keep shining! 🌞"
+    else:
+        return "Mood Detected: Unknown 🤔\n\nKeep going, you're doing great!"
+
+# Load pre-trained model and tokenizer
+@st.cache_resource
+def load_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, model = load_model()
+
+# Set page config as the very first Streamlit command
+st.set_page_config(page_title="Mental Health & Wellness Assistant", layout="wide")
+
+# Display header
+st.title("Mental Health & Wellness Assistant")
+
+# User input for text (emotion detection)
+user_input = st.text_area("How are you feeling today?", "Enter your thoughts here...")
+
+# Model prediction
+if user_input:
+    pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
+    result = pipe(user_input)
+
+    # Extracting the emotion from the model's result
+    emotion = result[0]['label']
+
+    # Display emotion
+    st.write(f"**Emotion Detected:** {emotion}")
+    
+    # Provide suggestions based on the detected emotion
+    if emotion == 'joy':
+        st.write("You're feeling happy! Keep up the great mood!")
+        st.write("Useful Resources:")
+        st.markdown("[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
+        st.write("[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+        st.write("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
+        
+        st.write("Relaxation Videos:")
+        st.markdown("[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)")
+
+    elif emotion == 'anger':
+        st.write("You're feeling angry. It's okay to feel this way. Let's try to calm down.")
+        st.write("Useful Resources:")
+        st.markdown("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
+        st.write("[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)")
+        st.write("[Dealing with Anger](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+        
+        st.write("Relaxation Videos:")
+        st.markdown("[Watch on YouTube](https://youtu.be/MIc299Flibs)")
+
+    elif emotion == 'fear':
+        st.write("You're feeling fearful. Take a moment to breathe and relax.")
+        st.write("Useful Resources:")
+        st.markdown("[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)")
+        st.write("[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+        st.write("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
+        
+        st.write("Relaxation Videos:")
+        st.markdown("[Watch on YouTube](https://youtu.be/yGKKz185M5o)")
+
+    elif emotion == 'sadness':
+        st.write("You're feeling sad. It's okay to take a break.")
+        st.write("Useful Resources:")
+        st.markdown("[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)")
+        st.write("[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+        
+        st.write("Relaxation Videos:")
+        st.markdown("[Watch on YouTube](https://youtu.be/-e-4Kx5px_I)")
+
+    elif emotion == 'surprise':
+        st.write("You're feeling surprised. It's okay to feel neutral!")
+        st.write("Useful Resources:")
+        st.markdown("[Managing Stress](https://www.health.harvard.edu/health-a-to-z)")
+        st.write("[Coping Strategies](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)")
+        
+        st.write("Relaxation Videos:")
+        st.markdown("[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)")
+
+# Chatbot functionality
+def chatbot_interface():
+    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
+
+    chatbot = gr.Chatbot(label="Chat")
+    demo = gr.Interface(
+        chat,
+        [gr.Textbox(lines=1, label="Message"), "state"],
+        [chatbot, "state"],
+        allow_flagging="never",
+        title="Mental Health Chatbot",
+        description="Your personal mental health assistant.",
+    )
+    return demo
+
+# Launch the interfaces
+if __name__ == "__main__":
+    # Create a tabbed interface for different features
+    tabs = [
+        gr.TabItem("Sentiment Analysis", chatbot_ui()),
+        gr.TabItem("Emotion Detection", chatbot_ui()),
+        gr.TabItem("Google Places Search", chatbot_ui()),
+    ]
+
+    with gr.Blocks() as demo:
+        gr.Tabs(tabs)
+
+    demo.launch()