Update app.py

app.py

@@ -30,13 +30,12 @@ def download_model_NLP():
     sequence_length=128,)
     model = keras_nlp.models.GPT2CausalLM.from_preset(
     "gpt2_base_en", preprocessor=preprocessor)
-    return model
-    
     
-    #url = "https://drive.google.com/uc?id=1zUGAPg9RVgo7bWtf_-L9MXoXKldZjs1y"
-    #output = "CIFAR10_Xception_(ACC_0.9704__LOSS_0.0335_).h5"
-    #gdown.download(url, output, quiet=False)
-    #return output
+    output = "total.h5"
+    id = "1-KgcnP1ayWQ6l2-4h723JCYPoWxzOnU3"
+    gdown.download(id=id, output=output, quiet=False)    
+    model.load_weights(output)     
+    return model
 
 
 
@@ -86,20 +85,23 @@ def predict_class(image):
 def classify_image(image):
     results = predict_class(image)
     output = {labels.get(i): float(results[i]) for i in range(len(results))}
-    return output
+    result_NLP = model.generate("Deer is able to", max_length=100)
+    return output, result_NLP
 
 
 inputs = gr.inputs.Image(type="pil", label="Upload an image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
-outputs = gr.outputs.Label(num_top_classes=4)
+output_1 = gr.outputs.Label(num_top_classes=4)
 
 title = "<h1 style='text-align: center;'>Image Classifier</h1>"
 description = "Upload an image and get the predicted class."
 # css_code='body{background-image:url("file=wave.mp4");}'
 
+model_NLP  = download_model_NLP()
+
 gr.Interface(fn=classify_image, 
              inputs=inputs, 
-             outputs=outputs, 
+             outputs=[output_1, "text"], 
              title=title, 
              examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_bird.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]],
              # css=css_code,