Update app.py

app.py

@@ -7,42 +7,37 @@ import tensorflow as tf
 interpreter = tf.lite.Interpreter(model_path="model.tflite")
 interpreter.allocate_tensors()
 
-# Get input and output details
+# Input / Output details
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
 
 # Class labels
 class_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediat']
 CONFIDENCE_THRESHOLD = 0.7
-TEMPERATURE = 0.5  # 🔑 Sharpening factor (lower = more confident)
 
 def preprocess_image(image):
-    """Resize & normalize image for float32 TFLite model"""
     image = image.resize((224, 224)).convert("RGB")
     img_array = np.array(image, dtype=np.float32) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    return img_array
+    return np.expand_dims(img_array, axis=0)
 
 def predict_image(image):
     try:
         img_array = preprocess_image(image)
 
-        # Run inference
+        # Inference
         interpreter.set_tensor(input_details[0]['index'], img_array)
         interpreter.invoke()
         output = interpreter.get_tensor(output_details[0]['index'])
 
-        # Apply softmax with temperature scaling (sharper confidence)
-        probabilities = tf.nn.softmax(output[0] / TEMPERATURE).numpy()
-
-        # Top prediction
+        # Normal softmax
+        probabilities = tf.nn.softmax(output[0]).numpy()
         class_idx = int(np.argmax(probabilities))
         confidence = float(np.max(probabilities))
 
         if confidence >= CONFIDENCE_THRESHOLD:
             return f"✅ Prediction: {class_names[class_idx]} (Confidence: {confidence:.2f})"
         else:
-            # Show Top-2 predictions if low confidence
+            # Show top-2 if low confidence
             top2_idx = np.argsort(probabilities)[-2:][::-1]
             suggestion = ", ".join(
                 [f"{class_names[i]} ({probabilities[i]:.2f})" for i in top2_idx]
@@ -52,7 +47,6 @@ def predict_image(image):
     except Exception as e:
         return f"❌ Error: {str(e)}"
 
-# Gradio app
 gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil", label="Upload Cervical Cell Image"),