Update app.py

app.py

@@ -1,4 +1,4 @@
-import sklearn
+# import sklearn
 from os import O_ACCMODE
 import gradio as gr
 import joblib
@@ -41,15 +41,18 @@ def get_metadata(model_id):
     except requests.exceptions.HTTPError:
         return None
 
-classifier = pipeline("text-classification", model)
-                
-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)
+def get_prediction(model_id):
+
+    classifier = pipeline("text-classification", model=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 predict
 
 with app:
     gr.Markdown(
@@ -76,8 +79,8 @@ with app:
     with gr.Row():
         out_1 = gr.Textbox(label="Prediction for Model 1")
 
-    classifier = pipeline("text-classification", model=model_id_1)   
-    btn1.click(fn=predict, inputs=inp_1, outputs=out_1)
+    # classifier = pipeline("text-classification", model)   
+    btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1)
 
     gr.Markdown(
     """
@@ -89,6 +92,6 @@ with app:
     with gr.Row():
         out_2 = gr.Textbox(label="Prediction for Model 2")   
     classifier = pipeline("text-classification", model=model_id_2)    
-    btn2.click(fn=predict, inputs=inp_1, outputs=out_2)
+    btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2)
     
 app.launch()