Update app.py

app.py

@@ -1,5 +1,4 @@
-
-# import sklearn
+import sklearn
 from os import O_ACCMODE
 import gradio as gr
 import joblib
@@ -41,9 +40,10 @@ def get_metadata(model_id):
         return metadata
     except requests.exceptions.HTTPError:
         return None
+
+classifier = pipeline("text-classification", model="juliensimon/distilbert-amazon-shoe-reviews")
                 
-def predict(review, model_id):
-        classifier = pipeline("text-classification", model=model_id)
+def predict(review):
         prediction = classifier(review)
         print(prediction)
         stars = prediction[0]['label']
@@ -62,16 +62,18 @@ with app:
         inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
         out_2 = gr.Textbox(label="Prediction")
 
+    
     gr.Markdown(
     """
     Model Predictions
     """)
-    
+
     with gr.Row():
       model1_input = gr.Textbox(label="Model 1")
     with gr.Row():
         btn = gr.Button("Prediction for Model 1")
-    btn.click(fn=load_agent(model_id_1), inputs=inp_1, outputs=out_2)
+    classifier = pipeline("text-classification", model=model_id_1)   
+    btn.click(fn=predict, inputs=inp_1, outputs=out_2)
 
 
 
@@ -79,13 +81,7 @@ with app:
       model2_input = gr.Textbox(label="Model 2") 
     with gr.Row():
         btn = gr.Button("Prediction for Model 2")
-    btn.click(fn=load_agent(model_id_2), inputs=inp_1, outputs=out_2)
-
-
-    # app_button.click(load_agent, inputs=[model1_input, model2_input], outputs=[model1_name, model1_score_output, model2_name, model2_score_output])
-    
-    # examples = gr.Examples(examples=[["juliensimon/distilbert-amazon-shoe-reviews","juliensimon/distilbert-amazon-shoe-reviews"]],
-    #                        inputs=[model1_input, model2_input])
-
+    classifier = pipeline("text-classification", model=model_id_2)    
+    btn.click(fn=predict, inputs=inp_1, outputs=out_2)
     
 app.launch()