change the code

app.py

@@ -1,31 +1,34 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from tensorflow.keras.preprocessing import image
-
-# Load the trained model
-model = tf.keras.models.load_model("bone_xray_cnn_model.h5")
-
-# Function to predict
-def predict_bone_xray(img):
-    img = img.resize((224, 224))  # Resize image
-    img_array = image.img_to_array(img) / 255.0  # Normalize
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-
-    prediction = np.argmax(model.predict(img_array))
-
-    if prediction == 0:
-        return "Fractured Bone"
-    else:
-        return "Healthy Bone"
-
-# Create Gradio interface
-interface = gr.Interface(
-    fn=predict_bone_xray,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="Bone X-ray Classifier",
-    description="Upload an X-ray image to detect bone fractures."
-)
-
-interface.launch()
+import os
+os.system("pip install tensorflow gradio numpy pillow")  # Install required packages
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Load the trained model
+model = tf.keras.models.load_model("bone_xray_cnn_model.h5")
+
+# Prediction function
+def predict_bone_xray(img):
+    img = img.resize((224, 224))  # Resize image
+    img_array = image.img_to_array(img) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    prediction = np.argmax(model.predict(img_array))
+
+    if prediction == 0:
+        return "Fractured Bone"
+    else:
+        return "Healthy Bone"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_bone_xray,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Bone X-ray Classifier",
+    description="Upload an X-ray image to detect bone fractures."
+)
+
+interface.launch()