Upload app.py.py

app.py.py

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+# -*- coding: utf-8 -*-
+"""Meena_A_Multilingual_Chatbot (1).ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-IfUcnDUppyMArHonc_iesEcN2gSKU-j
+"""
+
+!pip3 install transformers
+!pip install -q translate
+!pip install polyglot
+
+!pip install Pyicu
+
+!pip install Morfessor
+!pip install pycld2
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from translate import Translator
+from polyglot.detect import Detector
+
+# model_name = "microsoft/DialoGPT-large"
+model_name = "microsoft/DialoGPT-large"
+# model_name = "microsoft/DialoGPT-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# # chatting 5 times with nucleus sampling & tweaking temperature
+# step=-1
+# while(True):
+#     step+=1
+#     # take user input
+#     text = input(">> You:>")
+#     detected_language=Detector(text,quiet=True).language.code
+#     translator=Translator(from_lang=detected_language,to_lang="en")
+#     translated_input=translator.translate(text)
+#     print(translated_input)
+#     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)
+#     print(output)
+#     translator=Translator(from_lang="en",to_lang=detected_language)
+#     translated_output=translator.translate(output)
+  
+#     print(f">> Meena:> {translated_output}")
+
+!pip install gradio
+
+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
+
+output_text=gr.Textbox()
+gr.Interface(generate_text,"textbox",output_text,title="Meena",
+             description="Meena- Amultilingual Chatbot").launch(debug=False,share=True)
+
+!gradio deploy