Update app.py

app.py

@@ -11,7 +11,8 @@ from huggingface_hub.repocard import metadata_load
 app = gr.Blocks()
 
 model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
-model_id_2 = "juliensimon/distilbert-amazon-shoe-reviews"
+model_id_2 = "microsoft/deberta-base"
+model_id_3 = "juliensimon/distilbert-amazon-shoe-reviews"
 
 def load_agent(model_id):
     """
@@ -19,11 +20,9 @@ def load_agent(model_id):
     """
     # Load the metrics
     metadata = get_metadata(model_id)
-
     # get predictions
     predictions = predict(model_id)
 
-
     return model_id, predictions
 
 
@@ -47,12 +46,11 @@ def get_prediction(model_id):
              
     def predict(review):
             prediction = classifier(review)
-            print(prediction)
-            # stars = prediction[0]['label']
-            # stars = (int)(stars.split('_')[1])+1
-            # score = 100*prediction[0]['score']
-            # return "{} {:.0f}%".format("\U00002B50"*stars, score)
-            return prediction
+            predictions = []
+            for p in prediction:
+                new_pred = print(p)
+                predictions.append(new_pred)
+            return predictions
     return predict
 
 with app:
@@ -79,20 +77,32 @@ with app:
         btn1 = gr.Button("Predict for Model 1")
     with gr.Row():
         out_1 = gr.Textbox(label="Prediction for Model 1")
-
-    # classifier = pipeline("text-classification", model)   
     btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1)
 
     gr.Markdown(
     """
-    Model 2 = juliensimon/distilbert-amazon-shoe-reviews
+    Model 2 = microsoft/deberta-base
     """)
 
     with gr.Row():
         btn2 = gr.Button("Predict for Model 2")
     with gr.Row():
-        out_2 = gr.Textbox(label="Prediction for Model 2")   
-    classifier = pipeline("text-classification", model=model_id_2)    
+        out_2 = gr.Textbox(label="Prediction for Model 2")      
     btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2)
+
+    gr.Markdown(
+    """
+    Model 3 = juliensimon/distilbert-amazon-shoe-reviews
+    """)
+
+    with gr.Row():
+        btn3 = gr.Button("Predict for Model 3")
+    with gr.Row():
+        out_3 = gr.Textbox(label="Prediction for Model 3")   
+    classifier = pipeline("text-classification", model=model_id_3)    
+    btn3.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_3)
+
+
+
     
 app.launch()