Update app.py

app.py

@@ -39,6 +39,7 @@ TOKEN = "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"
 
 
 pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
+ scores = []
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
@@ -69,7 +70,12 @@ def load_model(file_name: str, path:str,device: torch.device) -> nn.Module:
    
 def predict(text):
   return pipe(text)[0]["translation_text"]
- 
+
+def track_score(score):
+    scores.append(score)
+    top_scores = sorted(scores, reverse=True)[:3]
+    return top_scores
+
 def main():
     #torch.cuda.init()
     #if torch.cuda.is_initialized():
@@ -93,11 +99,11 @@ def main():
     latent = torch.randn((1, 512), device=device) 
     #img_out, _, _ = generator([latent])
     #imgs_arr = make_image(img_out)
-    iface = gr.Interface(
-      fn=predict, 
-      inputs='text',
-      outputs='text',
-      examples=['result'],
+    #iface = gr.Interface(
+      #fn=predict, 
+      #inputs='text',
+      #outputs='text',
+      #examples=['result'],
       #gr.outputs.Image(type='numpy', label='Output'),
         #title=TITLE,
         #description=DESCRIPTION,
@@ -105,13 +111,19 @@ def main():
         #theme=args.theme,
         #allow_flagging=args.allow_flagging,
         #live=args.live,
-    )
+    #)
     
-    iface.launch(
+    #iface.launch(
         #enable_queue=args.enable_queue,
         #server_port=args.port,
         #share=args.share,
+    #)
+    demo = gr.Interface(
+    track_score, 
+    gr.Number(label="Score"), 
+    gr.JSON(label="Top Scores")
     )
+    demo.launch()
     
 if __name__ == '__main__':
     main()