Update app.py

app.py

@@ -13,7 +13,16 @@ def load_data():
     
     # Encode diseases
     disease_dict = {
-        # ... (same as in your Streamlit code)
+        '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
     }
 
     df.replace({'prognosis': disease_dict}, inplace=True)
@@ -24,7 +33,6 @@ def load_data():
     
     return df, tr, disease_dict
 
-# Try loading the data and handle exceptions
 try:
     df, tr, disease_dict = load_data()
 except FileNotFoundError as e:
@@ -62,15 +70,15 @@ def predict_disease(model, symptoms):
     prediction = model.predict([input_test])[0]
     return list(disease_dict.keys())[list(disease_dict.values()).index(prediction)]
 
-# Main application function
+# Gradio Interface
 def app_function(name, symptom1, symptom2, symptom3, symptom4, symptom5):
     if not name.strip():
-        return ["Please enter the patient's name."]
+        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."]
+        return "Please select at least 3 symptoms for accurate prediction."
     
     results = []
     for model_name, (model, acc) in trained_models.items():
@@ -79,9 +87,9 @@ def app_function(name, symptom1, symptom2, symptom3, symptom4, symptom5):
         result += f" (Accuracy: {acc * 100:.2f}%)"
         results.append(result)
     
-    return results
+    return "\n\n".join(results)
 
-# Gradio Interface
+# Gradio Interface Setup
 iface = gr.Interface(
     fn=app_function,
     inputs=[
@@ -90,12 +98,12 @@ iface = gr.Interface(
         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")
+        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.",
-    allow_flagging="never"
+    article="**Caution:** This system is designed for informational purposes only. Please visit a healthcare provider for any medical concerns."
 )
 
 # Launch the Gradio application