Update app.py

app.py

@@ -1,50 +1,41 @@
 import gradio as gr
+import tensorflow as tf
+from PIL import Image, ImageOps
 import numpy as np
-from tensorflow.keras.models import load_model
-from PIL import Image
-import traceback
 
-# Load the trained model
-try:
-    model = load_model("pneumonia_cnn_model.h5", compile=False)
-    print("✅ Model loaded successfully!")
-except Exception as e:
-    print("❌ Failed to load model:", e)
-    raise
-
-# Preprocessing function for uploaded X-ray images
-def preprocess_image(image):
-    image = image.convert("L")  # Convert to grayscale
-    image = image.resize((150, 150))  # Resize to model's expected input
-    image_array = np.array(image) / 255.0  # Normalize pixel values
-    image_array = np.expand_dims(image_array, axis=-1)  # Add channel dimension
-    image_array = np.expand_dims(image_array, axis=0)   # Add batch dimension
-    print("📷 Preprocessed image shape:", image_array.shape)
-    return image_array
+# Load the model
+model = tf.keras.models.load_model("pneumonia_cnn_model.h5")
 
 # Prediction function
 def predict(image):
     try:
-        image_array = preprocess_image(image)
-        prediction = model.predict(image_array)[0][0]
-        label = "🦠 Pneumonia" if prediction > 0.5 else "✅ Normal"
-        confidence = prediction if prediction > 0.5 else 1 - prediction
-        return f"{label} ({confidence * 100:.2f}% confidence)"
+        # Convert to grayscale
+        img = ImageOps.grayscale(image)
+        
+        # Resize image to match the model's expected input size (280x280)
+        img = img.resize((280, 280))  # Adjusted the size to 280x280
+        
+        # Convert to numpy array and normalize the pixel values
+        img_array = np.array(img).reshape(1, 280, 280, 1) / 255.0
+        
+        # Make prediction
+        prediction = model.predict(img_array)
+        
+        # Interpret prediction
+        if prediction >= 0.5:
+            return "Pneumonia detected"
+        else:
+            return "No pneumonia detected"
     except Exception as e:
-        traceback_str = traceback.format_exc()
-        print("❌ Prediction error:\n", traceback_str)
         return f"Error during prediction: {str(e)}"
 
-# Gradio interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Chest X-ray"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="🫁 Pneumonia Detection from Chest X-rays",
-    description="Upload a chest X-ray image to detect if the person has pneumonia using a deep learning model.",
-    theme="default",
-    allow_flagging="never"
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,  # Function to call on image input
+    inputs=gr.Image(type="pil", label="Upload Chest X-ray Image"),
+    outputs="text",  # Output is the prediction result (text)
+    live=True  # Optional: set to False if you don't want to update results live
 )
 
-# Launch interface (for Hugging Face Spaces)
-interface.launch(debug=True)
+# Launch the app
+iface.launch()