Update app.py

app.py

@@ -1,50 +1,60 @@
 import gradio as gr
 import tensorflow as tf
 import numpy as np
-from keras import backend as K
-from tensorflow.keras.preprocessing.image import img_to_array
-from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras import backend as K
 
-# --- Custom Metrics ---
+# ---------------------------
+# Custom metrics definitions
+# ---------------------------
 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)))
-    return true_positives / (possible_positives + K.epsilon())
+    recall = true_positives / (possible_positives + K.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)))
-    return true_positives / (predicted_positives + K.epsilon())
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
 
 def f1(y_true, y_pred):
-    prec = precision(y_true, y_pred)
-    rec = recall(y_true, y_pred)
-    return 2 * ((prec * rec) / (prec + rec + K.epsilon()))
+    precision1 = precision(y_true, y_pred)
+    recall1 = recall(y_true, y_pred)
+    return 2*((precision1*recall1)/(precision1+recall1+K.epsilon()))
 
-# --- Load the Model ---
-model = tf.keras.models.load_model(
-    "pneumonia_cnn_model.h5",
-    custom_objects={'precision': precision, 'recall': recall, 'f1': f1}
-)
+# ---------------------------
+# Load model with custom objects
+# ---------------------------
+model = load_model("model.h5", custom_objects={'f1': f1, 'precision': precision, 'recall': recall})
 
-# --- Prediction Function ---
+# ---------------------------
+# Prediction function
+# ---------------------------
 def predict_pneumonia(img):
     img = img.convert('L')  # Convert to grayscale
     img = img.resize((299, 299))
-    img_array = img_to_array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)  # Shape: (1, 299, 299, 1)
-    prediction = model.predict(img_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)"
-
-# --- Gradio Interface ---
-interface = gr.Interface(
-    fn=predict_pneumonia,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Text(),
-    title="Pneumonia Detection from Chest X-ray",
-    description="Upload a chest X-ray image to detect Pneumonia using a CNN model trained on grayscale 299x299 images."
-)
+    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, 
+                         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.")
+
+# ---------------------------
+# Launch
+# ---------------------------
 interface.launch()