Update app.py

app.py

@@ -1,60 +1,46 @@
-import gradio as gr
 import tensorflow as tf
-import numpy as np
+import gradio as gr
 from tensorflow.keras.models import load_model
+import numpy as np
 from tensorflow.keras.preprocessing import image
-from tensorflow.keras import backend as K
 
-# ---------------------------
-# Custom metrics definitions
-# ---------------------------
+# ---- Define custom metrics ----
 def recall(y_true, y_pred):
-    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
-    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
-    recall = true_positives / (possible_positives + K.epsilon())
+    true_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1)))
+    possible_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_true, 0, 1)))
+    recall = true_positives / (possible_positives + tf.keras.backend.epsilon())
     return recall
 
 def precision(y_true, y_pred):
-    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
-    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
-    precision = true_positives / (predicted_positives + K.epsilon())
+    true_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_pred, 0, 1)))
+    precision = true_positives / (predicted_positives + tf.keras.backend.epsilon())
     return precision
 
 def f1(y_true, y_pred):
-    precision1 = precision(y_true, y_pred)
-    recall1 = recall(y_true, y_pred)
-    return 2*((precision1*recall1)/(precision1+recall1+K.epsilon()))
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    return 2 * ((p * r) / (p + r + tf.keras.backend.epsilon()))
 
-# ---------------------------
-# Load model with custom objects
-# ---------------------------
+# ---- Load the model ----
 model = load_model("pneumonia_cnn_model.h5", custom_objects={'f1': f1, 'precision': precision, 'recall': recall})
 
-# ---------------------------
-# Prediction function
-# ---------------------------
-def predict_pneumonia(img):
-    img = img.convert('L')  # Convert to grayscale
-    img = img.resize((299, 299))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0
-
-    prediction = model.predict(img_array)
-    label = "PNEUMONIA" if prediction[0][0] > 0.5 else "NORMAL"
-    confidence = prediction[0][0] if prediction[0][0] > 0.5 else 1 - prediction[0][0]
-
-    return f"Prediction: {label} ({confidence*100:.2f}%)"
-
-# ---------------------------
-# Gradio interface
-# ---------------------------
-interface = gr.Interface(fn=predict_pneumonia, 
+# ---- Define prediction function ----
+def predict_image(img):
+    img = img.resize((150, 150))  # Make sure this matches your training image size
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0
+    prediction = model.predict(img)[0][0]
+    label = "Pneumonia" if prediction > 0.5 else "Normal"
+    return f"Prediction: {label} ({prediction:.2f})"
+
+# ---- Create Gradio Interface ----
+interface = gr.Interface(fn=predict_image,
                          inputs=gr.Image(type="pil"),
                          outputs="text",
-                         title="Pneumonia Detection Model",
-                         description="Upload a chest X-ray image to predict whether the patient has Pneumonia or not.")
+                         title="Pneumonia Detection",
+                         description="Upload a chest X-ray image to predict if it shows signs of pneumonia.")
 
-# ---------------------------
-# Launch
-# ---------------------------
+# ---- Launch ----
 interface.launch()