Update app.py

app.py

@@ -58,33 +58,54 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 
 import gradio as gr
 
-def generate_text(text):
-  step=-1
-  while(True):
-      step+=1
-      detected_language=Detector(text,quiet=True).language.code
-      translator=Translator(from_lang=detected_language,to_lang="en")
-      translated_input=translator.translate(text)
-      
-      if text.lower().find("bye")!=-1:
-        print(f">> Meena:> Bye Bye!")
-        break;
-      # encode the input and add end of string token
-      input_ids = tokenizer.encode(translated_input+tokenizer.eos_token, return_tensors="pt")
-      # concatenate new user input with chat history (if there is)
-      bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
-      # generate a bot response
-      chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id,do_sample=True,top_p=0.9,top_k=50,temperature=0.7,num_beams=5,no_repeat_ngram_size=2)
-      #print the output
-      output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
-      
-      translator=Translator(from_lang="en",to_lang=detected_language)
-      translated_output=translator.translate(output)
-    
-      return translated_output
+with gr.Blocks() as meena:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear") 
+    def set(chat_history_ids1):
+        global chat_history_ids
+        chat_history_ids=chat_history_ids1
+    def get():
+        return chat_history_ids
+    def set2(step1):
+        global step
+        step=step1
+    def get2():
+        return step
+    def generate_text(text,chat_history):
+        step=-1
+        if len(chat_history)==0:
+            step=-1
+        else:
+            step=get2()
+        step+=1
+        set2(step)
+        print(step)
+        if step!=0:
+            chat_history_ids=get()
+        detected_language=Detector(text,quiet=True).language.code
+        translator=Translator(from_lang=detected_language,to_lang="en")
+        translated_input=translator.translate(text)
+        # encode the input and add end of string token
+        input_ids=tokenizer.encode(translated_input+tokenizer.eos_token,return_tensors="pt")
+        # concatenate new user input with chat history (if there is)
+        bot_input_ids=torch.cat([chat_history_ids,input_ids],dim=-1) if step>0 else input_ids
+        # generate a bot response
+        chat_history_ids=model.generate(bot_input_ids,max_length=1000,pad_token_id=tokenizer.eos_token_id,do_sample=True,top_p=0.9,top_k=50,temperature=0.7,num_beams=5,no_repeat_ngram_size=2)
+        print(chat_history_ids)
+        set(chat_history_ids)
+        #print the output
+        output=tokenizer.decode(chat_history_ids[:,bot_input_ids.shape[-1]:][0],skip_special_tokens=True)  
+        translator=Translator(from_lang="en",to_lang=detected_language)
+        translated_output=translator.translate(output)
+        chat_history.append((text,translated_output))
+        if step==5:
+            set(-1)
+            set2(-1)
 
-output_text=gr.Textbox()
-gr.Interface(generate_text,"textbox",output_text,title="Meena",
-             description="Meena- A Multilingual Chatbot").launch(debug=False)
+        return "",chat_history
 
-#!gradio deploy
+    msg.submit(generate_text, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+meena.queue().launch()