Update app.py

app.py

@@ -2,20 +2,26 @@ import gradio as gr
 import numpy as np
 import tensorflow as tf
 
-# Assuming your model is loaded here
-model = tf.keras.models.load_model("pain_analysis.h5")
+# Load your model here
+model = tf.keras.models.load_model("pain_analysis.h5")  # Ensure you replace this with your actual model path
 
 def predict(image):
     image = np.array(image) / 255.0  # Normalize the image
-    # Make predictions using your model here
-    # result = model.predict(image)
-    return result
+    # Ensure the image is in the shape your model expects
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    # Make predictions using your model
+    result = model.predict(image)  # Perform prediction
 
-# Change this line to use gr.Image directly
+    # If your model outputs a probability distribution, you might want to take the argmax
+    predicted_class = np.argmax(result, axis=1)
+
+    return predicted_class  # Return the predicted class or whatever output format you need
+
+# Use gr.Image directly for inputs
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy"),  # Use gr.Image directly
     outputs="label"  # Specify the output type as needed
 )
 
-iface.launch()
+iface.launch(share=True)  # Set share=True to create a public link