Update app.py

app.py

@@ -1,20 +1,48 @@
+import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
+# Carregar o modelo e tokenizer
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-# Let's chat for 5 lines
-for step in range(5):
-    # encode the new user input, add the eos_token and return a tensor in Pytorch
-    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+# Função para gerar resposta
+def generate_response(user_input, chat_history=None):
+    if chat_history is None:
+        chat_history = []
+    
+    # Codificar a entrada do usuário
+    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+    
+    # Concatenar a entrada do usuário com o histórico da conversa
+    if chat_history:
+        bot_input_ids = torch.cat([chat_history, new_user_input_ids], dim=-1)
+    else:
+        bot_input_ids = new_user_input_ids
+    
+    # Gerar resposta
+    response_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    
+    # Decodificar a resposta
+    response = tokenizer.decode(response_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    
+    # Atualizar o histórico da conversa
+    chat_history = response_ids
+    
+    return response, chat_history
 
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+# Interface Gradio
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=["text"],
+    outputs=["text"],
+    title="DialoGPT Conversa",
+    description="Converse com o modelo DialoGPT",
+    allow_flagging="never"
+)
 
-    # generated a response while limiting the total chat history to 1000 tokens,
-    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-
-    # pretty print last ouput tokens from bot
-    print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+# Inicializar o histórico da conversa
+chat_history = None
 
+# Lançar a interface
+demo.launch()