Update app.py

app.py

@@ -2,7 +2,9 @@ import os
 import gradio as gr
 import nltk
 import numpy as np
-import tflearn
+import tensorflow as tf
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
 import random
 import json
 import pickle
@@ -35,14 +37,20 @@ with open("intents.json") as file:
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build the chatbot model
-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)
-chatbot_model = tflearn.DNN(net)
-chatbot_model.load("MentalHealthChatBotmodel.tflearn")
+# Build the chatbot model using TensorFlow 2.x Keras
+chatbot_model = Sequential([
+    Dense(8, input_shape=(len(training[0]),), activation='relu'),
+    Dense(8, activation='relu'),
+    Dense(len(output[0]), activation='softmax')
+])
+chatbot_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Load the saved model or train if not present
+if os.path.exists("MentalHealthChatBotmodel.h5"):
+    chatbot_model = load_model("MentalHealthChatBotmodel.h5")
+else:
+    chatbot_model.fit(training, output, epochs=1000, batch_size=8, verbose=1)
+    chatbot_model.save("MentalHealthChatBotmodel.h5")
 
 # Hugging Face sentiment and emotion models
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
@@ -57,37 +65,21 @@ emotion_pipeline = pipeline("text-classification", model=model_emotion, tokenize
 gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
 # Load the disease dataset
-df_train = pd.read_csv("Training.csv")  # Change the file path as necessary
-df_test = pd.read_csv("Testing.csv")    # Change the file path as necessary
-
-# Encode diseases dictionary (optional, currently unused directly)
-disease_dict = {
-    'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
-    'Peptic ulcer disease': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
-    'Hypertension ': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
-    'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'hepatitis A': 19,
-    'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23, 'Alcoholic hepatitis': 24,
-    'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemorrhoids(piles)': 28,
-    'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
-    'Hypoglycemia': 33, 'Osteoarthritis': 34, 'Arthritis': 35,
-    '(vertigo) Paroxysmal Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
-    'Psoriasis': 39, 'Impetigo': 40
-}
+df_train = pd.read_csv("Training.csv")
+df_test = pd.read_csv("Testing.csv")
 
 # Label encoder for consistent train/test encoding
 label_encoder = LabelEncoder()
 
 def prepare_data(df, is_train=True):
-    """Prepares data for training/testing with consistent label encoding."""
-    X = df.iloc[:, :-1]  # Features
-    y = df.iloc[:, -1]   # Target
+    X = df.iloc[:, :-1]
+    y = df.iloc[:, -1]
     if is_train:
         y_encoded = label_encoder.fit_transform(y)
     else:
         y_encoded = label_encoder.transform(y)
     return X, y_encoded
 
-# Preparing training and testing data with the same label encoder
 X_train, y_train = prepare_data(df_train, is_train=True)
 X_test, y_test = prepare_data(df_test, is_train=False)
 
@@ -109,7 +101,6 @@ for model_name, model_obj in models.items():
 # --- Helper Functions for Chatbot ---
 
 def bag_of_words(s, words):
-    """Convert user input to bag-of-words vector."""
     bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
@@ -120,10 +111,9 @@ def bag_of_words(s, words):
     return np.array(bag)
 
 def generate_chatbot_response(message, history):
-    """Generate chatbot response and maintain conversation history."""
     history = history or []
     try:
-        result = chatbot_model.predict([bag_of_words(message, words)])
+        result = chatbot_model.predict(np.array([bag_of_words(message, words)]), verbose=0)
         tag = labels[np.argmax(result)]
         response = "I'm sorry, I didn't understand that. 🤔"
         for intent in intents_data["intents"]:
@@ -136,7 +126,6 @@ def generate_chatbot_response(message, history):
     return history, response
 
 def analyze_sentiment(user_input):
-    """Analyze sentiment and map to emojis."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -145,7 +134,6 @@ def analyze_sentiment(user_input):
     return f"Sentiment: {sentiment_map[sentiment_class]}"
 
 def detect_emotion(user_input):
-    """Detect emotions based on input using cached pipeline."""
     result = emotion_pipeline(user_input)
     emotion = result[0]["label"].lower().strip()
     emotion_map = {
@@ -159,7 +147,6 @@ def detect_emotion(user_input):
     return emotion_map.get(emotion, "Unknown 🤔"), emotion
 
 def generate_suggestions(emotion):
-    """Return relevant suggestions based on detected emotions."""
     emotion_key = emotion.lower()
     suggestions = {
         "joy": [
@@ -206,11 +193,9 @@ def generate_suggestions(emotion):
     return "\n".join(formatted_suggestions)
 
 def get_health_professionals_and_map(location, query):
-    """Search nearby healthcare professionals using Google Maps API."""
     try:
         if not location or not query:
-            return [], ""  # Return empty list if inputs are missing
-
+            return [], ""
         geo_location = gmaps.geocode(location)
         if geo_location:
             lat, lng = geo_location[0]["geometry"]["location"].values()
@@ -229,7 +214,6 @@ def get_health_professionals_and_map(location, query):
         logging.error(f"Error fetching health professionals: {e}")
         return [], ""
 
-# Main Application Logic for Chatbot
 def app_function_chatbot(user_input, location, query, history):
     chatbot_history, _ = generate_chatbot_response(user_input, history)
     sentiment_result = analyze_sentiment(user_input)
@@ -238,25 +222,19 @@ def app_function_chatbot(user_input, location, query, history):
     professionals, map_html = get_health_professionals_and_map(location, query)
     return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
 
-# Disease Prediction Logic
 def predict_disease(symptoms):
-    """Predict disease based on input symptoms."""
-    valid_symptoms = [s for s in symptoms if s is not None]  # Filter out None values
+    valid_symptoms = [s for s in symptoms if s is not None]
     if len(valid_symptoms) < 3:
         return "Please select at least 3 symptoms for a better prediction."
-
-    input_test = np.zeros(len(X_train.columns))  # Create an array for feature input
+    input_test = np.zeros(len(X_train.columns))
     for symptom in valid_symptoms:
         if symptom in X_train.columns:
             input_test[X_train.columns.get_loc(symptom)] = 1
-
     predictions = {}
     for model_name, info in trained_models.items():
         prediction = info['model'].predict([input_test])[0]
         predicted_disease = label_encoder.inverse_transform([prediction])[0]
         predictions[model_name] = predicted_disease
-
-    # Create a Markdown table for displaying predictions
     markdown_output = [
         "### Predicted Diseases",
         "| Model | Predicted Disease |",
@@ -264,10 +242,8 @@ def predict_disease(symptoms):
     ]
     for model_name, disease in predictions.items():
         markdown_output.append(f"| {model_name} | {disease} |")
-
     return "\n".join(markdown_output)
 
-# CSS for the animated welcome message and improved styles
 welcome_message = """
 <style>
     @keyframes fadeIn {
@@ -302,10 +278,8 @@ welcome_message = """
 <div id="welcome-message">Welcome to the Well-Being Companion!</div>
 """
 
-# Gradio Application Interface
 with gr.Blocks(theme="shivi/calm_seafoam") as app:
-    gr.HTML(welcome_message)  # Animated welcome message
-
+    gr.HTML(welcome_message)
     with gr.Tab("Well-Being Chatbot"):
         gr.HTML("""
         <h1 style="color: #388e3c; font-family: 'Helvetica', sans-serif; text-align: center; font-size: 3.5em; margin-bottom: 0;">
@@ -323,8 +297,6 @@ with gr.Blocks(theme="shivi/calm_seafoam") as app:
             <li>👉 Receive emotional support suggestions based on your chat.</li>
         </ul>
         """)
-
-        # Infographics with images
         gr.HTML("""
         <div class="info-graphic">
             <img src="https://i.imgur.com/3ixjqBf.png" alt="Wellness Image 1">
@@ -332,44 +304,35 @@ with gr.Blocks(theme="shivi/calm_seafoam") as app:
             <img src="https://i.imgur.com/hcYAUJ3.png" alt="Wellness Image 3">
         </div>
         """)
-
         with gr.Row():
             user_input = gr.Textbox(label="Please Enter Your Message Here", placeholder="Type your message here...", max_lines=3)
             location = gr.Textbox(label="Please Enter Your Current Location", placeholder="E.g., Honolulu", max_lines=1)
             query = gr.Textbox(label="Search Health Professionals Nearby", placeholder="E.g., Health Professionals", max_lines=1)
-
         with gr.Row():
             submit_chatbot = gr.Button(value="Submit Your Message", variant="primary")
             clear_chatbot = gr.Button(value="Clear", variant="secondary")
-
         chatbot = gr.Chatbot(label="Chat History", show_label=True)
         sentiment = gr.Textbox(label="Detected Sentiment", show_label=True)
         emotion = gr.Textbox(label="Detected Emotion", show_label=True)
-
         professionals = gr.DataFrame(
             label="Nearby Health Professionals",
             headers=["Name", "Address"],
             value=[]
         )
-
         suggestions_markdown = gr.Markdown(label="Suggestions")
         map_html = gr.HTML(label="Interactive Map")
-
         def clear_input():
             return "", []
-
         submit_chatbot.click(
             app_function_chatbot,
             inputs=[user_input, location, query, chatbot],
             outputs=[chatbot, sentiment, emotion, suggestions_markdown, professionals, map_html],
         )
-
         clear_chatbot.click(
             clear_input,
             inputs=None,
             outputs=[user_input, chatbot]
         )
-
     with gr.Tab("Disease Prediction"):
         gr.HTML("""
         <h1 style="color: #388e3c; font-family: 'Helvetica', sans-serif; text-align: center; font-size: 3.5em; margin-bottom: 0;">
@@ -384,23 +347,18 @@ with gr.Blocks(theme="shivi/calm_seafoam") as app:
             <li>👉 Review the results displayed below!</li>
         </ul>
         """)
-
         symptom1 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 1", value=None)
         symptom2 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 2", value=None)
         symptom3 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 3", value=None)
         symptom4 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 4", value=None)
         symptom5 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 5", value=None)
-
         submit_disease = gr.Button(value="Predict Disease", variant="primary")
-
         disease_prediction_result = gr.Markdown(label="Predicted Diseases")
-
         submit_disease.click(
             lambda sym1, sym2, sym3, sym4, sym5: predict_disease([sym1, sym2, sym3, sym4, sym5]),
             inputs=[symptom1, symptom2, symptom3, symptom4, symptom5],
             outputs=disease_prediction_result
         )
 
-# Launch the Gradio application
 if __name__ == "__main__":
     app.launch(share=True)