Update app.py

app.py

@@ -1,84 +1,84 @@
-# import numpy as np
-# from tensorflow.keras.models import load_model
-# from tensorflow.keras.preprocessing import image
-
-# # Load trained model
-# model_path = r"Icream_pizza.model.h5"
-# model = load_model(model_path)
-
-# print("Model Loaded Successfully!")
-
-# # Image path
-# img_path = r"test_digit.png"
-
-# # Load image
-# img = image.load_img(img_path, target_size=(150, 150))
-# img_array = image.img_to_array(img) / 255.0
-# img_array = np.expand_dims(img_array, axis=0)
-
-# # Predict
-# prediction = model.predict(img_array)[0][0]
-
-# # Binary class probabilities
-# class_1_prob = float(prediction)          # sigmoid output
-# class_0_prob = 1 - class_1_prob
-
-# print("\nBoth Class Probabilities:\n")
-# print(f"Class 0 Probability: {class_0_prob * 100:.2f}%")
-# print(f"Class 1 Probability: {class_1_prob * 100:.2f}%")
-
-# # Final predicted class
-# if prediction >= 0.5:
-#     print("\nPredicted Class: 1")
-#     print(f"Confidence: {class_1_prob * 100:.2f}%")
-# else:
-#     print("\nPredicted Class: 0")
-#     print(f"Confidence: {class_0_prob * 100:.2f}%")
-
-
-# gradio app
-import numpy as np
-import gradio as gr
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-
-# Load model once
-model = load_model("pizza.model.h5")
-
-
-def predict(img):
-    # Resize to model input size
-    img = img.resize((150, 150))
-    
-    # Convert to array
-    img_array = image.img_to_array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-
-    # Prediction
-    pred = model.predict(img_array)[0][0]
-
-    class_1_prob = float(pred)
-    class_0_prob = 1 - class_1_prob
-
-    if pred >= 0.5:
-        label = "Class 1 (Pizza)"
-    else:
-        label = "Class 0"
-
-    return {
-        "Class 0 Probability": f"{class_0_prob * 100:.2f}%",
-        "Class 1 Probability": f"{class_1_prob * 100:.2f}%",
-        "Prediction": label
-    }
-
-
-# Gradio UI
-app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="json",
-    title="Binary Image Classifier Pizza",
-    description="Upload an image to classify between 2 classes using CNN model"
-)
-
+# import numpy as np   #ldfjlad
+# from tensorflow.keras.models import load_model
+# from tensorflow.keras.preprocessing import image
+
+# # Load trained model
+# model_path = r"Icream_pizza.model.h5"
+# model = load_model(model_path)
+
+# print("Model Loaded Successfully!")
+
+# # Image path
+# img_path = r"test_digit.png"
+
+# # Load image
+# img = image.load_img(img_path, target_size=(150, 150))
+# img_array = image.img_to_array(img) / 255.0
+# img_array = np.expand_dims(img_array, axis=0)
+
+# # Predict
+# prediction = model.predict(img_array)[0][0]
+
+# # Binary class probabilities
+# class_1_prob = float(prediction)          # sigmoid output
+# class_0_prob = 1 - class_1_prob
+
+# print("\nBoth Class Probabilities:\n")
+# print(f"Class 0 Probability: {class_0_prob * 100:.2f}%")
+# print(f"Class 1 Probability: {class_1_prob * 100:.2f}%")
+
+# # Final predicted class
+# if prediction >= 0.5:
+#     print("\nPredicted Class: 1")
+#     print(f"Confidence: {class_1_prob * 100:.2f}%")
+# else:
+#     print("\nPredicted Class: 0")
+#     print(f"Confidence: {class_0_prob * 100:.2f}%")
+
+
+# gradio app
+import numpy as np
+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+
+# Load model once
+model = load_model("pizza.model.h5")
+
+
+def predict(img):
+    # Resize to model input size
+    img = img.resize((150, 150))
+    
+    # Convert to array
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Prediction
+    pred = model.predict(img_array)[0][0]
+
+    class_1_prob = float(pred)
+    class_0_prob = 1 - class_1_prob
+
+    if pred >= 0.5:
+        label = "Class 1 (Pizza)"
+    else:
+        label = "Class 0"
+
+    return {
+        "Class 0 Probability": f"{class_0_prob * 100:.2f}%",
+        "Class 1 Probability": f"{class_1_prob * 100:.2f}%",
+        "Prediction": label
+    }
+
+
+# Gradio UI
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="Binary Image Classifier Pizza",
+    description="Upload an image to classify between 2 classes using CNN model"
+)
+
 app.launch()