arreglos 2

app.py

@@ -1,56 +1,37 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
+from huggingface_hub import InferenceClient
 from typing import List, Tuple, Dict
-import torch
 
-# Load model and tokenizer
-model_name = "AuriLab/gpt-bi-instruct-cesar"
-tokenizer_name = "AuriLab/gpt-bi"
-tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+client = InferenceClient("AuriLab/gpt-bi-instruct-cesar")
 
-def format_messages(history: List[Tuple[str, str]], system_message: str, user_message: str) -> str:
-    # Format conversation history into a single string
-    formatted_prompt = system_message + "\n\n"
-    for user, assistant in history:
-        if user:
-            formatted_prompt += f"User: {user}\n"
-        if assistant:
-            formatted_prompt += f"Assistant: {assistant}\n"
-    formatted_prompt += f"User: {user_message}\nAssistant:"
-    return formatted_prompt
+def format_messages(history: List[Tuple[str, str]], system_message: str, user_message: str) -> List[Dict[str, str]]:
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend([
+        {"role": "user" if i % 2 == 0 else "assistant", "content": msg}
+        for turn in history
+        for i, msg in enumerate(turn)
+        if msg
+    ])
+    messages.append({"role": "user", "content": user_message})
+    return messages
 
-def respond(message: str, history: List[Tuple[str, str]]) -> str:
-    system_message = """You are a helpful assistant. Follow these rules:
-                    1. Provide diverse and varied responses
-                    2. Avoid repeating the same words or phrases
-                    3. Use synonyms and alternative expressions
-                    4. Be concise and direct"""
+def respond(message: str, history: List[Tuple[str, str]], system_message: str, max_tokens: int, temperature: float, top_p: float) -> str:
+    messages = format_messages(history, system_message, message)
+    response = ""
     
-    prompt = format_messages(history, system_message, message)
-    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
-    
-    # Generate response
-    with torch.no_grad():
-        outputs = model.generate(
-            inputs["input_ids"],
-            max_new_tokens=200,
-            temperature=0.7,
-            top_p=0.85,
-            do_sample=True,
-            pad_token_id=tokenizer.pad_token_id,
-            eos_token_id=tokenizer.eos_token_id,
-        )
-    
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    # Extract only the assistant's response
-    response = response.split("Assistant:")[-1].strip()
-    
-    return response
+    for msg in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=0.7,        # Aumentado para más variedad
+        top_p=0.85,            # Ajustado para mejor balance
+    ):
+        token = msg.choices[0].delta.content
+        response += token
+        yield response
 
-# Create the Gradio interface with custom title
 demo = gr.ChatInterface(
-    fn=respond,
+    respond,
     title="Demo GPT-BI instruct",
 )