Create app.py

Final App Updated

app.py

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+import os
+import gradio as gr
+import nltk
+import numpy as np
+import tflearn
+import random
+import json
+import pickle
+from nltk.tokenize import word_tokenize
+from nltk.stem.lancaster import LancasterStemmer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import googlemaps
+import folium
+import torch
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
+
+# Suppress TensorFlow warnings
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Download necessary NLTK resources
+nltk.download("punkt")
+stemmer = LancasterStemmer()
+
+# Load intents and chatbot training data
+with open("intents.json") as file:
+    intents_data = json.load(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")
+
+# Hugging Face sentiment and emotion models
+tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+
+# Google Maps API Client
+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
+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
+}
+
+# Function to prepare data
+def prepare_data(df):
+    """Prepares data for training/testing."""
+    X = df.iloc[:, :-1]  # Features
+    y = df.iloc[:, -1]   # Target
+    label_encoder = LabelEncoder()
+    y_encoded = label_encoder.fit_transform(y)
+    return X, y_encoded, label_encoder
+
+# Preparing training and testing data
+X_train, y_train, label_encoder_train = prepare_data(df_train)
+X_test, y_test, label_encoder_test = prepare_data(df_test)
+
+# Define the models
+models = {
+    "Decision Tree": DecisionTreeClassifier(),
+    "Random Forest": RandomForestClassifier(),
+    "Naive Bayes": GaussianNB()
+}
+
+# Train and evaluate models
+trained_models = {}
+for model_name, model_obj in models.items():
+    model_obj.fit(X_train, y_train)  # Fit the model
+    y_pred = model_obj.predict(X_test)  # Make predictions
+    acc = accuracy_score(y_test, y_pred)  # Calculate accuracy
+    trained_models[model_name] = {'model': model_obj, 'accuracy': acc}
+
+# 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()]
+    for se in s_words:
+        for i, w in enumerate(words):
+            if w == se:
+                bag[i] = 1
+    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)])
+        tag = labels[np.argmax(result)]
+        response = "I'm sorry, I didn't understand that. 🤔"
+        for intent in intents_data["intents"]:
+            if intent["tag"] == tag:
+                response = random.choice(intent["responses"])
+                break
+    except Exception as e:
+        response = f"Error: {e}"
+    history.append((message, response))
+    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)
+    sentiment_class = torch.argmax(outputs.logits, dim=1).item()
+    sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
+    return f"Sentiment: {sentiment_map[sentiment_class]}"
+
+def detect_emotion(user_input):
+    """Detect emotions based on input."""
+    pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
+    result = pipe(user_input)
+    emotion = result[0]["label"].lower().strip()
+    emotion_map = {
+        "joy": "Joy 😊",
+        "anger": "Anger 😠",
+        "sadness": "Sadness 😢",
+        "fear": "Fear 😨",
+        "surprise": "Surprise 😲",
+        "neutral": "Neutral 😐",
+    }
+    return emotion_map.get(emotion, "Unknown 🤔"), emotion
+
+def generate_suggestions(emotion):
+    """Return relevant suggestions based on detected emotions."""
+    emotion_key = emotion.lower()
+    suggestions = {
+        "joy": [
+            ("Mindfulness Practices", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"),
+            ("Coping with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"),
+            ("Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"),
+            ("Relaxation Video", "https://youtu.be/yGKKz185M5o"),
+        ],
+        "anger": [
+            ("Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"),
+            ("Stress Management Tips", "https://www.health.harvard.edu/health-a-to-z"),
+            ("Dealing with Anger", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"),
+            ("Relaxation Video", "https://youtu.be/MIc299Flibs"),
+        ],
+        "fear": [
+            ("Mindfulness Practices", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"),
+            ("Coping with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"),
+            ("Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"),
+            ("Relaxation Video", "https://youtu.be/yGKKz185M5o"),
+        ],
+        "sadness": [
+            ("Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"),
+            ("Dealing with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"),
+            ("Relaxation Video", "https://youtu.be/-e-4Kx5px_I"),
+        ],
+        "surprise": [
+            ("Managing Stress", "https://www.health.harvard.edu/health-a-to-z"),
+            ("Coping Strategies", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"),
+            ("Relaxation Video", "https://youtu.be/m1vaUGtyo-A"),
+        ],
+    }
+
+    # Create a markdown string for clickable suggestions in a table format
+    formatted_suggestions = ["### Suggestions"]
+    formatted_suggestions.append(f"Since you’re feeling {emotion}, you might find these links particularly helpful. Don’t hesitate to explore:")
+    formatted_suggestions.append("| Title | Link |")
+    formatted_suggestions.append("|-------|------|")  # Table headers
+    formatted_suggestions += [
+        f"| {title} | [{link}]({link}) |" for title, link in suggestions.get(emotion_key, [("No specific suggestions available.", "#")])
+    ]
+
+    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
+            
+        geo_location = gmaps.geocode(location)
+        if geo_location:
+            lat, lng = geo_location[0]["geometry"]["location"].values()
+            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
+            professionals = []
+            map_ = folium.Map(location=(lat, lng), zoom_start=13)
+            for place in places_result:
+                professionals.append([place['name'], place.get('vicinity', 'No address provided')])
+                folium.Marker(
+                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
+                    popup=f"{place['name']}"
+                ).add_to(map_)
+            return professionals, map_._repr_html_()
+        return [], ""  # Return empty list if no professionals found
+    except Exception as e:
+        return [], ""  # Return empty list on exception
+
+# 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)
+    emotion_result, cleaned_emotion = detect_emotion(user_input)
+    suggestions = generate_suggestions(cleaned_emotion)
+    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
+    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
+    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_train.inverse_transform([prediction])[0]
+        predictions[model_name] = predicted_disease
+
+    # Create a Markdown table for displaying predictions
+    markdown_output = ["### Predicted Diseases"]
+    markdown_output.append("| Model | Predicted Disease |")
+    markdown_output.append("|-------|------------------|")  # Table headers
+    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 {
+        0% { opacity: 0; }
+        100% { opacity: 1; }
+    }
+    #welcome-message {
+        font-size: 2em;
+        font-weight: bold;
+        text-align: center;
+        animation: fadeIn 3s ease-in-out;
+        margin-bottom: 20px;
+    }
+    .info-graphic {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        margin: 20px 0;
+    }
+    .info-graphic img {
+        width: 150px;  /* Adjust size as needed */
+        height: auto;  /* Keep aspect ratio */
+        margin: 0 10px;  /* Space between images */
+    }
+    h1 {
+        text-align: center;  /* Center-align the main title */
+        font-size: 3em;  /* Increase title size */
+        color: #004d40;  /* Use your theme's color */
+        margin-bottom: 20px;  /* Space below the title */
+    }
+</style>
+<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
+
+    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;">
+            🌼 Well-Being Companion 🌼
+        </h1>
+        <p style="color: #4caf50; font-family: 'Helvetica', sans-serif; text-align: center; font-size: 1.5em; margin-top: 0;">
+            Your Trustworthy Guide to Emotional Wellness and Health
+        </p>
+        <h2 style="color: #2e7d32; font-family: 'Helvetica', sans-serif; text-align: center; font-size: 1.2em;">
+            🌈 Emotional Support | 🧘🏻‍♀️ Mindfulness | 🥗 Nutrition | 🏋️ Physical Health | 💤 Sleep Hygiene
+        </h2>
+        <ul style="text-align: center; color: #2e7d32;">
+            <li>👉 Enter your messages in the input box to chat with our well-being companion.</li>
+            <li>👉 Share your current location to find nearby health professionals.</li>
+            <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">
+            <img src="https://i.imgur.com/Nvljr1A.png" alt="Wellness Image 2">
+            <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():  # Align Submit and Clear buttons side by side
+            submit_chatbot = gr.Button(value="Submit Your Message", variant="primary")
+            clear_chatbot = gr.Button(value="Clear", variant="secondary")  # Clear button
+
+        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)
+
+        # Apply styles and create the DataFrame
+        professionals = gr.DataFrame(
+            label="Nearby Health Professionals",  # Use label parameter to set the title
+            headers=["Name", "Address"],
+            value=[]  # Initialize with empty data
+        )
+
+        suggestions_markdown = gr.Markdown(label="Suggestions")
+        map_html = gr.HTML(label="Interactive Map")
+
+        # Functionality to clear the chat input
+        def clear_input():
+            return "", []  # Clear both the user input and chat history
+
+        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]  # Reset user input and chat history
+        )
+
+    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;">
+            Disease Prediction
+        </h1>
+        <p style="color: #4caf50; font-family: 'Helvetica', sans-serif; text-align: center; font-size: 1.5em; margin-top: 0;">
+            Help us understand your symptoms!
+        </p>
+        <ul style="text-align: center; color: #2e7d32;">
+            <li>👉 Select at least 3 symptoms from the dropdown lists.</li>
+            <li>👉 Click on "Predict Disease" to see potential conditions.</li>
+            <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 symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease(
+                [symptom1, symptom2, symptom3, symptom4, symptom5]),
+            inputs=[symptom1, symptom2, symptom3, symptom4, symptom5],
+            outputs=disease_prediction_result
+        )
+
+# Launch the Gradio application
+app.launch()