Update app.py

app.py

@@ -1,110 +1,289 @@
+import os
 import gradio as gr
-import pandas as pd
+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.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score
 
-# Load datasets
-def load_data():
-    df = pd.read_csv("Training.csv")
-    tr = pd.read_csv("Testing.csv")
-    
-    # Encode diseases
-    disease_dict = {
-        'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
-        'Peptic ulcer diseae': 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 hemmorhoids(piles)': 28,
-        'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
-        'Hypoglycemia': 33, 'Osteoarthristis': 34, 'Arthritis': 35,
-        '(vertigo) Paroymsal  Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
-        'Psoriasis': 39, 'Impetigo': 40
-    }
+# 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")
 
-    df.replace({'prognosis': disease_dict}, inplace=True)
-    df = df.infer_objects(copy=False)
+# Google Maps API Client
+gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
-    tr.replace({'prognosis': disease_dict}, inplace=True)
-    tr = tr.infer_objects(copy=False)
+# 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):
+    # Split the dataset into features and target
+    X = df.iloc[:, :-1]  # All columns except the last one (features)
+    y = df.iloc[:, -1]   # The last column (target)
+
+    # Encode the target variable
+    label_encoder = LabelEncoder()
+    y_encoded = label_encoder.fit_transform(y)
     
-    return df, tr, disease_dict
-
-try:
-    df, tr, disease_dict = load_data()
-except FileNotFoundError as e:
-    raise RuntimeError("Data files not found. Please ensure `Training.csv` and `Testing.csv` are available.")
-except Exception as e:
-    raise RuntimeError(f"An error occurred while loading data: {e}")
-
-l1 = list(df.columns[:-1])  
-X = df[l1]
-y = df['prognosis']
-X_test = tr[l1]
-y_test = tr['prognosis']
-
-# Trained models
-def train_models():
-    models = {
-        "Decision Tree": DecisionTreeClassifier(),
-        "Random Forest": RandomForestClassifier(),
-        "Naive Bayes": GaussianNB()
+    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 😐",
     }
-    trained_models = {}
-    for model_name, model_obj in models.items():
-        model_obj.fit(X, y)
-        acc = accuracy_score(y_test, model_obj.predict(X_test))
-        trained_models[model_name] = (model_obj, acc)
-    return trained_models
+    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
+    formatted_suggestions = [
+        f"- [{title}]({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
 
-trained_models = train_models()
+# 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
 
-def predict_disease(model, symptoms):
-    input_test = np.zeros(len(l1))
+# Disease Prediction Logic
+def predict_disease(symptoms):
+    """Predict disease based on input symptoms."""
+    input_test = np.zeros(len(X_train.columns))  # Create an array for feature input
     for symptom in symptoms:
-        if symptom in l1:
-            input_test[l1.index(symptom)] = 1
-    prediction = model.predict([input_test])[0]
-    return list(disease_dict.keys())[list(disease_dict.values()).index(prediction)]
-
-# Gradio Interface
-def app_function(name, symptom1, symptom2, symptom3, symptom4, symptom5):
-    if not name.strip():
-        return "Please enter the patient's name."
-    
-    symptoms_selected = [s for s in [symptom1, symptom2, symptom3, symptom4, symptom5] if s != "None"]
-    
-    if len(symptoms_selected) < 3:
-        return "Please select at least 3 symptoms for accurate prediction."
-    
-    results = []
-    for model_name, (model, acc) in trained_models.items():
-        prediction = predict_disease(model, symptoms_selected)
-        result = f"{model_name} Prediction: Predicted Disease: **{prediction}**"
-        result += f" (Accuracy: {acc * 100:.2f}%)"
-        results.append(result)
+        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
+    return predictions
+
+# Gradio Application Interface
+with gr.Blocks() as app:
+    gr.HTML("<h1>🌟 Well-Being Companion</h1>")
     
-    return "\n\n".join(results)
-
-# Gradio Interface Setup
-iface = gr.Interface(
-    fn=app_function,
-    inputs=[
-        gr.Textbox(label="Name of Patient"),
-        gr.Dropdown(["None"] + l1, label="Symptom 1"),
-        gr.Dropdown(["None"] + l1, label="Symptom 2"),
-        gr.Dropdown(["None"] + l1, label="Symptom 3"),
-        gr.Dropdown(["None"] + l1, label="Symptom 4"),
-        gr.Dropdown(["None"] + l1, label="Symptom 5"),
-    ],
-    outputs=gr.Textbox(label="Prediction"),
-    title="Disease Predictor Using Machine Learning",
-    description="For accurate results, please select at least 3 symptoms.",
-    article="**Caution:** This system is designed for informational purposes only. Please visit a healthcare provider for any medical concerns."
-)
+    with gr.Tab("Mental Health Chatbot"):
+        with gr.Row():
+            user_input = gr.Textbox(label="Please Enter Your Message Here")
+            location = gr.Textbox(label="Please Enter Your Current Location Here")
+            query = gr.Textbox(label="Please Enter Which Health Professional You Want To Search Nearby")
+        
+        submit_chatbot = gr.Button(value="Submit Chatbot", variant="primary")
+
+        chatbot = gr.Chatbot(label="Chat History")
+        sentiment = gr.Textbox(label="Detected Sentiment")
+        emotion = gr.Textbox(label="Detected Emotion")
+        
+        suggestions_markdown = gr.Markdown(label="Suggestions")  # Use Markdown to display clickable links
+        professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])
+        map_html = gr.HTML(label="Interactive Map")
+
+        submit_chatbot.click(
+            app_function_chatbot,
+            inputs=[user_input, location, query, chatbot],
+            outputs=[chatbot, sentiment, emotion, suggestions_markdown, professionals, map_html],
+        )
+
+    with gr.Tab("Disease Prediction"):
+        symptom1 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 1")
+        symptom2 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 2")
+        symptom3 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 3")
+        symptom4 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 4")
+        symptom5 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 5")
+        
+        submit_disease = gr.Button(value="Predict Disease", variant="primary")
+        disease_prediction_result = gr.Textbox(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
-iface.launch()
+app.launch()