Update app.py

app.py

@@ -1,8 +1,7 @@
-# Step 1: Import the necessary toolkits
 import gradio as gr
 from transformers import pipeline
 
-# Step 2: Load our AI Model
+# Step 1: Load our AI Model
 print("Loading the Health Analysis model...")
 health_classifier = pipeline(
     "text-classification",
@@ -10,25 +9,18 @@ health_classifier = pipeline(
 )
 print("Model loaded successfully!")
 
-# NEW: Step 2.5 - Create the Label Dictionary
-# The model outputs generic names like 'label_0'. This dictionary maps them to real disease names.
-label_map = {
-    'label_0': 'Fungal infection', 'label_1': 'Allergy', 'label_2': 'GERD', 
-    'label_3': 'Chronic cholestasis', 'label_4': 'Drug Reaction', 'label_5': 'Peptic ulcer disease', 
-    'label_6': 'AIDS', 'label_7': 'Diabetes', 'label_8': 'Gastroenteritis', 
-    'label_9': 'Bronchial Asthma', 'label_10': 'Hypertension', 'label_11': 'Migraine', 
-    'label_12': 'Cervical spondylosis', 'label_13': 'Paralysis (brain hemorrhage)', 
-    'label_14': 'Jaundice', 'label_15': 'Malaria', 'label_16': 'Chicken pox', 
-    'label_17': 'Dengue', 'label_18': 'Typhoid', 'label_19': 'Hepatitis A', 
-    'label_20': 'Hepatitis B', 'label_21': 'Hepatitis C', 'label_22': 'Hepatitis D', 
-    'label_23': 'Hepatitis E', 'label_24': 'Alcoholic hepatitis', 'label_25': 'Tuberculosis', 
-    'label_26': 'Common Cold', 'label_27': 'Pneumonia', 'label_28': 'Dimorphic hemmorhoids(piles)', 
-    'label_29': 'Heart attack', 'label_30': 'Varicose veins', 'label_31': 'Hypothyroidism', 
-    'label_32': 'Hyperthyroidism', 'label_33': 'Hypoglycemia', 'label_34': 'Osteoarthristis', 
-    'label_35': 'Arthritis', 'label_36': '(vertigo) Paroymsal Positional Vertigo', 
-    'label_37': 'Acne', 'label_38': 'Urinary tract infection', 'label_39': 'Psoriasis', 
-    'label_40': 'Impetigo'
-}
+# Step 2: Define the main function for the app
+def analyze_symptoms(symptoms):
+    # The pipeline returns a list containing one dictionary, e.g., [{'label': 'Allergy', 'score': 0.98}]
+    # We select the first item from the list to get the dictionary.
+    prediction_dict = health_classifier(symptoms)[0]
+    
+    # Extract the disease name and score from the dictionary.
+    disease = prediction_dict['label']
+    confidence_score = prediction_dict['score']
+    
+    # Format the results into a clean, readable string for the user.
+    return f"Predicted Condition: {disease}\nConfidence Score: {confidence_score:.2f}"
 
 # Step 3: Define the content for our app
 app_title = "Symptom to Disease Classifier"