Nilesh Ranjan Pal
commited on
Commit
•
f094795
1
Parent(s):
1f3f3b7
Create app.py
Browse files
app.py
ADDED
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import streamlit as st
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import numpy as np
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from nltk.sentiment import SentimentIntensityAnalyzer
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.models import load_model
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import json
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# Load the model and tokenizer
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model_path = 'model.h5'
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tokenizer_path = 'tokenizer.json'
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# Load the tokenizer
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with open(tokenizer_path, 'r') as tokenizer_file:
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word_index = json.load(tokenizer_file)
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tokenizer = Tokenizer(num_words=100000)
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tokenizer.word_index = word_index
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classes = ['ADHD', 'OCD', 'aspergers', 'depression', 'ptsd']
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# Disease definitions
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disease_definitions = {
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'ADHD': "Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by persistent patterns of inattention, hyperactivity, and impulsivity.",
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'OCD': "Obsessive-Compulsive Disorder (OCD) is a mental health disorder characterized by recurring unwanted thoughts (obsessions) and repetitive behaviors or mental rituals (compulsions).",
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'aspergers': "Asperger's Syndrome, also known as Autism Spectrum Disorder (ASD), is a developmental disorder characterized by difficulties in social interaction, repetitive patterns of behavior, and limited interests or activities.",
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'depression': "Depression is a common mental health disorder characterized by persistent sadness, loss of interest or pleasure in activities, changes in appetite or sleep patterns, and feelings of worthlessness or guilt.",
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'ptsd': "Post-Traumatic Stress Disorder (PTSD) is a mental health condition triggered by a traumatic event. Symptoms may include flashbacks, nightmares, severe anxiety, and uncontrollable thoughts about the event."
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}
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# Coping strategies
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coping_strategies = {
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'ADHD': 'Try breaking tasks into smaller, manageable steps and using reminders or organizational tools.',
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'OCD': 'Practice exposure and response prevention (ERP) techniques and consider therapy options like Cognitive Behavioral Therapy (CBT).',
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'aspergers': 'Focus on building social skills through therapy and practice. Seek support from autism support groups.',
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'depression': 'Reach out to a mental health professional for therapy or medication options. Engage in self-care activities and reach out to supportive friends or family.',
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'ptsd': 'Consider therapy options such as Cognitive Processing Therapy (CPT) or Eye Movement Desensitization and Reprocessing (EMDR). Practice grounding techniques during moments of distress.'
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}
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# ... (Rest of the code remains the same)
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# Resources
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resources = {
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'ADHD': 'Vidyasagar Institute of Mental Health and Neurosciences: http://www.vimhanshospital.org/',
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'OCD': 'Serenity Neuropsychiatry Clinic: http://serenityclinic.org/',
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'aspergers': 'Autism Society of India: http://www.autismsocietyofindia.org/',
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'depression': 'iCALL - Psychosocial Helpline: https://www.icallhelpline.org/',
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'ptsd': 'Mpower - The Centre: https://www.mpowerminds.com/'
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}
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# Doctors
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doctors = {
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'ADHD': ['Dr. John Doe', 'Dr. Jane Smith'],
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'OCD': ['Dr. David Johnson', 'Dr. Emily Davis'],
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'aspergers': ['Dr. Michael Brown', 'Dr. Sarah Wilson'],
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'depression': ['Dr. Christopher Lee', 'Dr. Samantha Taylor'],
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'ptsd': ['Dr. Robert Martinez', 'Dr. Jessica Thompson']
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}
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# Podcasts
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podcasts = {
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'ADHD': ['ADHD Experts Podcast', 'The ADHD Manual Podcast'],
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'OCD': ['The OCD Stories Podcast', 'The OCD & Anxiety Show'],
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'aspergers': ['The Autism Helper Podcast', 'Autism Spectrum Podcast'],
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'depression': ['The Depression Files Podcast', 'The Hilarious World of Depression'],
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'ptsd': ['The Trauma Therapist Podcast', 'PTSD Poetry Podcast']
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}
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# Books
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books = {
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'ADHD': ['Driven to Distraction by Edward M. Hallowell', 'Taking Charge of Adult ADHD by Russell A. Barkley'],
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'OCD': ['Brain Lock by Jeffrey M. Schwartz', 'The OCD Workbook by Bruce M. Hyman'],
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'aspergers': ['The Complete Guide to Asperger\'s Syndrome by Tony Attwood', 'Uniquely Human by Barry M. Prizant'],
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'depression': ['The Noonday Demon by Andrew Solomon', 'Lost Connections by Johann Hari'],
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'ptsd': ['The Body Keeps the Score by Bessel van der Kolk', 'After the War Zone by Laurie B. Slone']
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}
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# Music
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music = {
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'ADHD': ['Focus Playlist on Spotify', 'Energetic Beats Playlist on Apple Music'],
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'OCD': ['Relaxing Sounds Playlist on Spotify', 'Calm Vibes Playlist on Apple Music'],
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'aspergers': ['Soothing Melodies Playlist on Spotify', 'Peaceful Piano Playlist on Apple Music'],
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'depression': ['Mood Booster Playlist on Spotify', 'Feel-Good Hits Playlist on Apple Music'],
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'ptsd': ['Healing Sounds Playlist on Spotify', 'Emotional Support Playlist on Apple Music']
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}
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def get_response(user_input):
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if 'hello' in user_input.lower() or 'hi' in user_input.lower():
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return "Hello! How can I assist you today?"
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if 'thank you' in user_input.lower() or 'thanks' in user_input.lower() or 'thanx' in user_input.lower():
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return "You are welcome!"
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# Check if the user asks for the definition of a disease
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disease_keywords = ['definition', 'what is', 'explain', "what's", 'whats']
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for keyword in disease_keywords:
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if keyword in user_input.lower():
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for disease, definition in disease_definitions.items():
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if disease.lower() in user_input.lower():
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return f"The definition of {disease} is: {definition}"
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sequence = tokenizer.texts_to_sequences([user_input])
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sequence = pad_sequences(sequence, maxlen=100)
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# Load the model
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model = load_model(model_path)
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prediction = model.predict(sequence)
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predicted_class_index = np.argmax(prediction)
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predicted_class = classes[predicted_class_index]
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# Calculate sentiment score
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sid = SentimentIntensityAnalyzer()
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sentiment_score = sid.polarity_scores(user_input)['compound']
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# Determine the topic based on the highest predicted probability
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predicted_topic = classes[np.argmax(prediction)]
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if predicted_topic in classes:
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# Provide coping strategies, resources, doctors, podcasts, books, and music based on the predicted topic
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if np.max(prediction) < 0.7:
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return f"The model predicts your mental health issue to be {predicted_class}, but it's recommended to consult a doctor for a proper diagnosis."
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response = f"Predicted Disease: {predicted_class}\n"
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if predicted_topic in coping_strategies:
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response += f"Coping Strategy: {coping_strategies[predicted_topic]}\n"
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if predicted_topic in resources:
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response += f"Resource: {resources[predicted_topic]}\n"
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if predicted_topic in doctors:
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response += f"Doctors: {', '.join(doctors[predicted_topic])}\n"
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if predicted_topic in podcasts:
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response += f"Podcasts: {', '.join(podcasts[predicted_topic])}\n"
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if predicted_topic in books:
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response += f"Books: {', '.join(books[predicted_topic])}\n"
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if predicted_topic in music:
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response += f"Music: {', '.join(music[predicted_topic])}\n"
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return response
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if sentiment_score >= 0.2:
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return "I'm glad to hear that you're feeling happy! If you need any mental health support, feel free to share."
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elif sentiment_score <= -0.2:
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return "I'm sorry to hear that you're feeling down. Remember, you're not alone. Reach out to someone you trust or consider seeking professional help."
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return "Thank you for sharing. How can I assist you today?"
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def main():
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st.title("Health Chatbot")
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st.write("Enter your message and get a response from the chatbot:")
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user_input = st.text_input("User Input:")
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if st.button("Submit"):
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response = get_response(user_input)
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st.write("Bot Response:")
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st.write(response)
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if __name__ == '__main__':
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main()
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