Update app.py

app.py

@@ -1,25 +1,26 @@
+import torch
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "microsoft/DialoGPT-medium"  # ممكن تغيره حسب رغبتك
+model_name = "microsoft/DialoGPT-medium"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
 chat_history_ids = None
 
-def chat_with_bot(user_input, history=[]):
+def respond(message, chat_history=[]):
     global chat_history_ids
-    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
+    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')
 
     if chat_history_ids is not None:
-        input_ids = torch.cat([chat_history_ids, input_ids], dim=-1)
+        input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
+    else:
+        input_ids = new_input_ids
 
-    output = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-    chat_history_ids = output
+    chat_history_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
 
-    response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
-    return response
+    reply = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return reply
 
-iface = gr.Interface(fn=chat_with_bot, inputs="text", outputs="text", title="ChatBot 🤖")
-iface.launch()
+gr.Interface(fn=respond, inputs="text", outputs="text", title="DialoGPT Chatbot").launch()