Update app.py

app.py

@@ -13,9 +13,7 @@ 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.preprocessing import LabelEncoder
 from sklearn.metrics import accuracy_score
 
 # Suppress TensorFlow warnings
@@ -51,7 +49,11 @@ model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/e
 # Google Maps API Client
 gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
-# Disease dictionary to map disease names to numerical values
+# 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,
@@ -65,6 +67,37 @@ disease_dict = {
     '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 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."""
@@ -191,149 +224,22 @@ 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
 
-# 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))
+# 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)]
-
-# 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 for the Gradio Interface
-custom_css = """
-body {
-    font-family: 'Roboto', sans-serif;
-    background-color: #3c6487; /* Set the background color */
-    color: white;
-}
-
-h1 {
-    background: #ffffff;
-    color: #000000;
-    border-radius: 8px;
-    padding: 10px;
-    font-weight: bold;
-    text-align: center;
-    font-size: 2.5rem;
-}
-
-textarea, input {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    padding: 8px;
-    font-size: 1rem;
-    caret-color: black;
-    outline: none;
-    border-radius: 8px;
-}
-
-textarea:focus, input:focus {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-    outline: none;
-}
-
-textarea:hover, input:hover {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-}
-
-.df-container {
-    background: white;
-    color: black;
-    border: 2px solid orange;
-    border-radius: 10px;
-    padding: 10px;
-    font-size: 14px;
-    max-height: 400px;
-    height: auto;
-    overflow-y: auto;
-}
-
-#suggestions-title {
-    text-align: center !important; /* Ensure the centering is applied */
-    font-weight: bold !important; /* Ensure bold is applied */
-    color: white !important; /* Ensure color is applied */
-    font-size: 4.2rem !important; /* Ensure font size is applied */
-    margin-bottom: 20px !important; /* Ensure margin is applied */
-}
-
-/* Style for the submit button */
-.gr-button {
-    background-color: #ae1c93;  /* Set the background color to #ae1c93 */
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1), 0 2px 4px rgba(0, 0, 0, 0.06);
-    transition: background-color 0.3s ease;
-}
-
-.gr-button:hover {
-    background-color: #8f167b;
-}
-
-.gr-button:active {
-    background-color: #7f156b;
-}
-"""
-
-# Gradio Application
-with gr.Blocks(css=custom_css) as app:
+        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>")
     
     with gr.Tab("Mental Health Chatbot"):
@@ -361,19 +267,19 @@ with gr.Blocks(css=custom_css) as app:
         )
 
     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")
+        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(
-            app_function_disease,
-            inputs=[patient_name, symptom1, symptom2, symptom3, symptom4, symptom5],
+            lambda symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease(
+                [symptom1, symptom2, symptom3, symptom4, symptom5]),
+            inputs=[symptom1, symptom2, symptom3, symptom4, symptom5],
             outputs=disease_prediction_result,
         )