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Running on Zero
Running on Zero
| import gradio as gr | |
| import spaces | |
| from huggingface_hub import login | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import os | |
| # AUTENTICACIÓN CORRECTA PARA GEMMA EN SPACES | |
| hf_token = os.getenv("HF_CLASSIFIER") | |
| if hf_token: | |
| login(token=hf_token) | |
| print("Autenticado con HF_CLASSIFIER") | |
| else: | |
| print("ERROR: HF_CLASSIFIER no encontrado en Secrets") | |
| # Carga modelo y tokenizer | |
| model_id = "google/gemma-3-4b-it" | |
| print(f"Cargando {model_id}...") | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.bfloat16, | |
| low_cpu_mem_usage=True | |
| ) | |
| SYSTEM_PROMPT = """You are an expert assistant that analyzes user messages. | |
| ALWAYS respond ONLY with valid JSON in this EXACT format: | |
| { | |
| "summary": "Brief and clear summary of the user's message (max 1-2 sentences)", | |
| "tags": ["tag1", "tag2", "tag3"], | |
| "sentiment": { | |
| "score": -1.0, | |
| "label": "NEGATIVE | NEUTRAL | POSITIVE", | |
| "explanation": "Brief reason for the analysis" | |
| } | |
| } | |
| RULES: | |
| - summary: Capture the main essence | |
| - tags: 3-5 relevant keywords (themes, emotions, entities) | |
| - sentiment: score (-1.0=negative, 0=neutral, +1.0=positive) | |
| - NEVER add text outside the JSON | |
| - JSON must be valid and parseable""" | |
| def generate_response(message, history): | |
| model.to('cuda') | |
| messages = [] | |
| for user_msg, ai_msg in history: | |
| messages.append({"role": "user", "content": user_msg}) | |
| if ai_msg: | |
| messages.append({"role": "assistant", "content": ai_msg}) | |
| messages.append({"role": "user", "content": "System: "+SYSTEM_PROMPT+" User:"+message}) | |
| prompt = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=1512, # Aumentado para evitar respuestas cortadas (truncadas) | |
| do_sample=True, | |
| temperature=0.7, | |
| pad_token_id=tokenizer.eos_token_id, | |
| eos_token_id=tokenizer.eos_token_id # Añadido para forzar fin completo y evitar truncado | |
| ) | |
| response = tokenizer.decode( | |
| outputs[0][inputs.input_ids.shape[1]:], | |
| skip_special_tokens=True | |
| ).strip() | |
| model.to('cpu') | |
| torch.cuda.empty_cache() | |
| return response | |
| # Interfaz estándar genérica con Gradio ChatInterface (sin CSS personalizado ni Blocks, como pide la documentación oficial de Gradio) | |
| demo = gr.ChatInterface( | |
| fn=generate_response, | |
| title="Desxtools Crypto News Text Analyzer", | |
| description="Chatbot estándar con google/gemma-3-4b-it en ZeroGPU. Diseño genérico sin modificaciones.", | |
| cache_examples=True | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |