Update app.py

app.py

@@ -1,23 +1,21 @@
 import gradio as gr
+import numpy as np
 import tensorflow as tf
 
-# Load your model
-model = tf.keras.models.load_model("pain_analysis.h5")
+# Assuming your model is loaded here
+# model = tf.keras.models.load_model("pain_analysis.h5")
 
 def predict(image):
-    # Preprocess the image as required by your model
-    image = image.resize((150, 150))  # Assuming your model expects 150x150 input
     image = np.array(image) / 255.0  # Normalize the image
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    predictions = model.predict(image)
-    return predictions.tolist()  # Adjust the output format as needed
+    # Make predictions using your model here
+    # result = model.predict(image)
+    return result
 
-# Define the Gradio interface
-inputs = gr.Image(type="pil")  # Updated input definition
-outputs = gr.Label(num_top_classes=4)  # Adjust according to your model's output
+# Change this line to use gr.Image directly
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),  # Use gr.Image directly
+    outputs="label"  # Specify the output type as needed
+)
 
-interface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="Pain Analysis Model")
-
-# Launch the app
-if __name__ == "__main__":
-    interface.launch()
+iface.launch()