Create app.py

app.py

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+import streamlit as st
+import numpy as np
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from nltk.sentiment import SentimentIntensityAnalyzer
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.models import load_model
+import json
+
+# Load the model and tokenizer
+model_path = 'model.h5'
+tokenizer_path = 'tokenizer.json'
+
+# Load the tokenizer
+with open(tokenizer_path, 'r') as tokenizer_file:
+    word_index = json.load(tokenizer_file)
+    tokenizer = Tokenizer(num_words=100000)
+    tokenizer.word_index = word_index
+
+classes = ['ADHD', 'OCD', 'aspergers', 'depression', 'ptsd']
+
+# Disease definitions
+disease_definitions = {
+    'ADHD': "Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by persistent patterns of inattention, hyperactivity, and impulsivity.",
+    'OCD': "Obsessive-Compulsive Disorder (OCD) is a mental health disorder characterized by recurring unwanted thoughts (obsessions) and repetitive behaviors or mental rituals (compulsions).",
+    '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.",
+    '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.",
+    '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."
+}
+
+# Coping strategies
+coping_strategies = {
+    'ADHD': 'Try breaking tasks into smaller, manageable steps and using reminders or organizational tools.',
+    'OCD': 'Practice exposure and response prevention (ERP) techniques and consider therapy options like Cognitive Behavioral Therapy (CBT).',
+    'aspergers': 'Focus on building social skills through therapy and practice. Seek support from autism support groups.',
+    '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.',
+    'ptsd': 'Consider therapy options such as Cognitive Processing Therapy (CPT) or Eye Movement Desensitization and Reprocessing (EMDR). Practice grounding techniques during moments of distress.'
+}
+
+# ... (Rest of the code remains the same)
+# Resources
+resources = {
+    'ADHD': 'Vidyasagar Institute of Mental Health and Neurosciences: http://www.vimhanshospital.org/',
+    'OCD': 'Serenity Neuropsychiatry Clinic: http://serenityclinic.org/',
+    'aspergers': 'Autism Society of India: http://www.autismsocietyofindia.org/',
+    'depression': 'iCALL - Psychosocial Helpline: https://www.icallhelpline.org/',
+    'ptsd': 'Mpower - The Centre: https://www.mpowerminds.com/'
+}
+
+# Doctors
+doctors = {
+    'ADHD': ['Dr. John Doe', 'Dr. Jane Smith'],
+    'OCD': ['Dr. David Johnson', 'Dr. Emily Davis'],
+    'aspergers': ['Dr. Michael Brown', 'Dr. Sarah Wilson'],
+    'depression': ['Dr. Christopher Lee', 'Dr. Samantha Taylor'],
+    'ptsd': ['Dr. Robert Martinez', 'Dr. Jessica Thompson']
+}
+
+# Podcasts
+podcasts = {
+    'ADHD': ['ADHD Experts Podcast', 'The ADHD Manual Podcast'],
+    'OCD': ['The OCD Stories Podcast', 'The OCD & Anxiety Show'],
+    'aspergers': ['The Autism Helper Podcast', 'Autism Spectrum Podcast'],
+    'depression': ['The Depression Files Podcast', 'The Hilarious World of Depression'],
+    'ptsd': ['The Trauma Therapist Podcast', 'PTSD Poetry Podcast']
+}
+
+# Books
+books = {
+    'ADHD': ['Driven to Distraction by Edward M. Hallowell', 'Taking Charge of Adult ADHD by Russell A. Barkley'],
+    'OCD': ['Brain Lock by Jeffrey M. Schwartz', 'The OCD Workbook by Bruce M. Hyman'],
+    'aspergers': ['The Complete Guide to Asperger\'s Syndrome by Tony Attwood', 'Uniquely Human by Barry M. Prizant'],
+    'depression': ['The Noonday Demon by Andrew Solomon', 'Lost Connections by Johann Hari'],
+    'ptsd': ['The Body Keeps the Score by Bessel van der Kolk', 'After the War Zone by Laurie B. Slone']
+}
+
+# Music
+music = {
+    'ADHD': ['Focus Playlist on Spotify', 'Energetic Beats Playlist on Apple Music'],
+    'OCD': ['Relaxing Sounds Playlist on Spotify', 'Calm Vibes Playlist on Apple Music'],
+    'aspergers': ['Soothing Melodies Playlist on Spotify', 'Peaceful Piano Playlist on Apple Music'],
+    'depression': ['Mood Booster Playlist on Spotify', 'Feel-Good Hits Playlist on Apple Music'],
+    'ptsd': ['Healing Sounds Playlist on Spotify', 'Emotional Support Playlist on Apple Music']
+}
+
+def get_response(user_input):
+    if 'hello' in user_input.lower() or 'hi' in user_input.lower():
+        return "Hello! How can I assist you today?"
+
+    if 'thank you' in user_input.lower() or 'thanks' in user_input.lower() or 'thanx' in user_input.lower():
+        return "You are welcome!"
+
+    # Check if the user asks for the definition of a disease
+    disease_keywords = ['definition', 'what is', 'explain', "what's", 'whats']
+    for keyword in disease_keywords:
+        if keyword in user_input.lower():
+            for disease, definition in disease_definitions.items():
+                if disease.lower() in user_input.lower():
+                    return f"The definition of {disease} is: {definition}"
+
+    sequence = tokenizer.texts_to_sequences([user_input])
+    sequence = pad_sequences(sequence, maxlen=100)
+
+    # Load the model
+    model = load_model(model_path)
+
+    prediction = model.predict(sequence)
+    predicted_class_index = np.argmax(prediction)
+    predicted_class = classes[predicted_class_index]
+
+    # Calculate sentiment score
+    sid = SentimentIntensityAnalyzer()
+    sentiment_score = sid.polarity_scores(user_input)['compound']
+
+    # Determine the topic based on the highest predicted probability
+    predicted_topic = classes[np.argmax(prediction)]
+
+    if predicted_topic in classes:
+        # Provide coping strategies, resources, doctors, podcasts, books, and music based on the predicted topic
+        if np.max(prediction) < 0.7:
+            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."
+
+        response = f"Predicted Disease: {predicted_class}\n"
+
+        if predicted_topic in coping_strategies:
+            response += f"Coping Strategy: {coping_strategies[predicted_topic]}\n"
+
+        if predicted_topic in resources:
+            response += f"Resource: {resources[predicted_topic]}\n"
+
+        if predicted_topic in doctors:
+            response += f"Doctors: {', '.join(doctors[predicted_topic])}\n"
+
+        if predicted_topic in podcasts:
+            response += f"Podcasts: {', '.join(podcasts[predicted_topic])}\n"
+
+        if predicted_topic in books:
+            response += f"Books: {', '.join(books[predicted_topic])}\n"
+
+        if predicted_topic in music:
+            response += f"Music: {', '.join(music[predicted_topic])}\n"
+
+        return response
+
+    if sentiment_score >= 0.2:
+        return "I'm glad to hear that you're feeling happy! If you need any mental health support, feel free to share."
+    elif sentiment_score <= -0.2:
+        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."
+
+    return "Thank you for sharing. How can I assist you today?"
+
+def main():
+    st.title("Health Chatbot")
+    st.write("Enter your message and get a response from the chatbot:")
+
+    user_input = st.text_input("User Input:")
+
+    if st.button("Submit"):
+        response = get_response(user_input)
+        st.write("Bot Response:")
+        st.write(response)
+
+if __name__ == '__main__':
+    main()