Update app.py

app.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import random
 import argparse
 import functools
 import os
@@ -39,7 +40,6 @@ TOKEN = "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"
 
 
 pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
-scores = []
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
@@ -71,10 +71,20 @@ 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 chat(message, history):
+    history = history or []
+    message = message.lower()
+    if message.startswith("how many"):
+        response = random.randint(1, 10)
+    elif message.startswith("how"):
+        response = random.choice(["Great", "Good", "Okay", "Bad"])
+    elif message.startswith("where"):
+        response = random.choice(["Here", "There", "Somewhere"])
+    else:
+        response = "I don't know"
+    history.append((message, response))
+    return history, history
+
 
 def main():
     #torch.cuda.init()
@@ -118,10 +128,12 @@ def main():
         #server_port=args.port,
         #share=args.share,
     #)
+    chatbot = gr.Chatbot().style(color_map=("green", "pink"))
     demo = gr.Interface(
-        track_score, 
-        gr.Number(label="Score"), 
-        gr.JSON(label="Top Scores")
+        chat,
+        ["text", "state"],
+        [chatbot, "state"],
+        allow_flagging="never",
     )
     demo.launch()