Update app.py

app.py

@@ -12,6 +12,11 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 import googlemaps
 import folium
 import torch
+import pandas as pd
+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"
@@ -47,7 +52,7 @@ model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/e
 gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
 # Disease dictionary to map disease names to numerical values
-disease_dict = { 
+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,
@@ -60,7 +65,7 @@ disease_dict = {
     'Psoriasis': 39, 'Impetigo': 40
 }
 
-# Helper Functions
+# Helper Functions for Chatbot
 def bag_of_words(s, words):
     """Convert user input to bag-of-words vector."""
     bag = [0] * len(words)
@@ -112,17 +117,6 @@ def detect_emotion(user_input):
     }
     return emotion_map.get(emotion, "Unknown 🤔"), emotion
 
-def disease_prediction(user_input):
-    """Predict disease based on input symptoms."""
-    # Here, we simulate disease prediction logic
-    symptoms = user_input.lower().split()
-    disease_probabilities = [random.random() for _ in disease_dict]  # Placeholder for prediction model
-    
-    # Select the highest probability (for demonstration)
-    disease_index = np.argmax(disease_probabilities)
-    disease_name = list(disease_dict.keys())[disease_index]
-    return disease_name
-
 def generate_suggestions(emotion):
     """Return relevant suggestions based on detected emotions."""
     emotion_key = emotion.lower()
@@ -177,7 +171,6 @@ def get_health_professionals_and_map(location, query):
             professionals = []
             map_ = folium.Map(location=(lat, lng), zoom_start=13)
             for place in places_result:
-                # Use a list of values to append each professional
                 professionals.append([place['name'], place.get('vicinity', 'No address provided')])
                 folium.Marker(
                     location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
@@ -189,17 +182,79 @@ def get_health_professionals_and_map(location, query):
     except Exception as e:
         return [], ""  # Return empty list on exception
 
-# Main Application Logic
-def app_function(user_input, location, query, history):
+# 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)
-    disease_result = disease_prediction(user_input)  # Get disease prediction
-    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html, disease_result
+    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
+
+# Load datasets for Disease Prediction
+def load_data():
+    df = pd.read_csv("Training.csv")
+    tr = pd.read_csv("Testing.csv")
+    
+    # Encode diseases
+    df.replace({'prognosis': disease_dict}, inplace=True)
+    df = df.infer_objects(copy=False)
+    tr.replace({'prognosis': disease_dict}, inplace=True)
+    tr = tr.infer_objects(copy=False)
+    
+    return df, tr
+
+df, tr = load_data()
+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()
+    }
+    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
+
+trained_models = train_models()
+
+def predict_disease(model, symptoms):
+    input_test = np.zeros(len(l1))
+    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)]
+
+# Disease Prediction Application Logic
+def app_function_disease(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)
+    
+    return "\n\n".join(results)
 
-# CSS Styling
+# CSS Styling for the Gradio Interface
 custom_css = """
 body {
     font-family: 'Roboto', sans-serif;
@@ -280,30 +335,47 @@ textarea:hover, input:hover {
 # Gradio Application
 with gr.Blocks(css=custom_css) as app:
     gr.HTML("<h1>🌟 Well-Being Companion</h1>")
-    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 = gr.Button(value="Submit", variant="primary")
-
-    chatbot = gr.Chatbot(label="Chat History")
-    sentiment = gr.Textbox(label="Detected Sentiment")
-    emotion = gr.Textbox(label="Detected Emotion")
-    
-    # Adding Suggestions Title with Styled Markdown (Centered and Bold)
-    gr.Markdown("Suggestions", elem_id="suggestions-title")
-    
-    suggestions = gr.DataFrame(headers=["Title", "Link"])  # Table for suggestions
-    professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])  # Changed to DataFrame
-    map_html = gr.HTML(label="Interactive Map")
     
-    disease = gr.Textbox(label="Predicted Disease")  # Display disease prediction
-
-    submit.click(
-        app_function,
-        inputs=[user_input, location, query, chatbot],
-        outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html, disease],
-    )
+    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")
 
-app.launch()
+        chatbot = gr.Chatbot(label="Chat History")
+        sentiment = gr.Textbox(label="Detected Sentiment")
+        emotion = gr.Textbox(label="Detected Emotion")
+        
+        gr.Markdown("Suggestions", elem_id="suggestions-title")
+        
+        suggestions = gr.DataFrame(headers=["Title", "Link"])
+        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, professionals, map_html],
+        )
+
+    with gr.Tab("Disease Prediction"):
+        patient_name = gr.Textbox(label="Name of Patient")
+        symptom1 = gr.Dropdown(["None"] + l1, label="Symptom 1")
+        symptom2 = gr.Dropdown(["None"] + l1, label="Symptom 2")
+        symptom3 = gr.Dropdown(["None"] + l1, label="Symptom 3")
+        symptom4 = gr.Dropdown(["None"] + l1, label="Symptom 4")
+        symptom5 = gr.Dropdown(["None"] + l1, label="Symptom 5")
+
+        submit_disease = gr.Button(value="Submit Disease Prediction", variant="primary")
+        disease_prediction_result = gr.Textbox(label="Prediction")
+
+        submit_disease.click(
+            app_function_disease,
+            inputs=[patient_name, symptom1, symptom2, symptom3, symptom4, symptom5],
+            outputs=disease_prediction_result,
+        )
+
+# Launch the Gradio application
+app.launch()