Update app.py

app.py

@@ -76,27 +76,10 @@ def load_data():
 
     # Replace prognosis values with numerical categories
     df.replace({'prognosis': disease_dict}, inplace=True)
+    df['prognosis'] = df['prognosis'].astype(int)
 
-    # Check unique values in prognosis for debugging
-    print("Unique values in prognosis after mapping:", df['prognosis'].unique())
-
-    # Ensure prognosis is purely numerical after mapping
-    if df['prognosis'].dtype == 'object':  # Check for unmapped entries
-        raise ValueError(f"The prognosis contains unmapped values: {df['prognosis'].unique()}")
-
-    df['prognosis'] = df['prognosis'].astype(int)  # Convert to integer
-
-    df = df.infer_objects()  # Remove 'copy' argument
-    
-    # Similar process for the testing data
     tr.replace({'prognosis': disease_dict}, inplace=True)
-
-    # Ensure it is also numerical
-    if tr['prognosis'].dtype == 'object':
-        raise ValueError(f"Testing data prognosis contains unmapped values: {tr['prognosis'].unique()}")
-    
-    tr['prognosis'] = tr['prognosis'].astype(int)  # Convert to integer
-    tr = tr.infer_objects()  # Remove 'copy' argument
+    tr['prognosis'] = tr['prognosis'].astype(int)
 
     return df, tr, disease_dict
 
@@ -109,7 +92,7 @@ y_test = tr['prognosis']
 
 # Encode the target variable with LabelEncoder if still in string format
 le = LabelEncoder()
-y_encoded = le.fit_transform(y)  # Needs to be string labels if they were mixed types
+y_encoded = le.fit_transform(y)
 
 def train_models():
     models = {
@@ -119,7 +102,7 @@ def train_models():
     }
     trained_models = {}
     for model_name, model_obj in models.items():
-        model_obj.fit(X, y_encoded)  # Use encoded labels
+        model_obj.fit(X, y_encoded)
         acc = accuracy_score(y_test, model_obj.predict(X_test))
         trained_models[model_name] = (model_obj, acc)
     return trained_models
@@ -138,27 +121,6 @@ def predict_disease(model, symptoms):
         "confidence": confidence
     }
 
-def disease_prediction_interface(symptoms):
-    symptoms_selected = [s for s in symptoms 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_info = predict_disease(model, symptoms_selected)
-        predicted_disease = prediction_info["disease"]
-        confidence_score = prediction_info["confidence"]
-        
-        result = f"{model_name} Prediction: Predicted Disease: **{predicted_disease}**"
-        if confidence_score is not None:
-            result += f" (Confidence: {confidence_score:.2f})"
-        result += f" (Accuracy: {acc * 100:.2f}%)"
-        
-        results.append(result)
-
-    return results
-
 # Helper Functions (for chatbot)
 def bag_of_words(s, words):
     bag = [0] * len(words)
@@ -242,8 +204,26 @@ def app_function(user_input, location, query, symptoms, history):
     emotion_result, cleaned_emotion = detect_emotion(user_input)
     suggestions = generate_suggestions(cleaned_emotion)
     professionals, map_html = get_health_professionals_and_map(location, query)
-    disease_results = disease_prediction_interface(symptoms)
     
+    # Disease prediction logic
+    symptoms_selected = [s for s in symptoms if s != "None"]
+    if len(symptoms_selected) < 3:
+        disease_results = ["Please select at least 3 symptoms for accurate prediction."]
+    else:
+        results = []
+        for model_name, (model, acc) in trained_models.items():
+            prediction_info = predict_disease(model, symptoms_selected)
+            predicted_disease = prediction_info["disease"]
+            confidence_score = prediction_info["confidence"]
+            
+            result = f"{model_name} Prediction: Predicted Disease: **{predicted_disease}**"
+            if confidence_score is not None:
+                result += f" (Confidence: {confidence_score:.2f})"
+            result += f" (Accuracy: {acc * 100:.2f}%)"
+            
+            results.append(result)
+        disease_results = results
+
     return (
         chatbot_history, 
         sentiment_result, 
@@ -256,65 +236,7 @@ def app_function(user_input, location, query, symptoms, history):
 
 # CSS Styling
 custom_css = """
-body {
-    font-family: 'Roboto', sans-serif;
-    background-color: #3c6487;
-    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;
-}
-.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;
-    font-weight: bold !important;
-    color: white !important;
-    font-size: 4.2rem !important;
-    margin-bottom: 20px !important;
-}
-.gr-button {
-    background-color: #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;
-}
+/* Your custom CSS styles go here */
 """
 
 # Gradio Application
@@ -352,4 +274,5 @@ with gr.Blocks(css=custom_css) as app:
         outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html, disease_predictions],
     )
 
+# Launch the Gradio application
 app.launch()