Update app.py

app.py

@@ -6,23 +6,22 @@ import requests
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from dotenv import load_dotenv
 
-# --------- Carregar secrets -------------
+# ----------------- Load secrets -----------------
 load_dotenv()
-SUPABASE_URL = os.environ.get("SUPABASE_URL")  # Ex.: https://xyz.supabase.co
-SUPABASE_KEY = os.environ.get("SUPABASE_KEY")
+HF_TOKEN = os.environ.get("HF_TOKEN")              # Hugging Face Token
+SUPABASE_URL = os.environ.get("SUPABASE_URL")      # Supabase URL
+SUPABASE_KEY = os.environ.get("SUPABASE_KEY")      # Supabase API Key
+
 HEADERS = {
     "apikey": SUPABASE_KEY,
     "Authorization": f"Bearer {SUPABASE_KEY}",
     "Content-Type": "application/json"
 }
 
-# --------- Funções Supabase -------------
+# ----------------- Supabase functions -----------------
 def create_user(name: str):
     api_key = str(uuid.uuid4())
-    data = {
-        "name": name,
-        "api_key": api_key
-    }
+    data = {"name": name, "api_key": api_key}
     response = requests.post(f"{SUPABASE_URL}/rest/v1/users",
                              headers=HEADERS,
                              data=json.dumps(data))
@@ -37,22 +36,22 @@ def check_key(api_key: str):
     data = response.json()
     return len(data) > 0
 
-# --------- Carregar modelo Gemma 270M -------------
+# ----------------- Carregar modelo Gemma 270M -----------------
 model_name = "google/gemma-3-270m-it"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=HF_TOKEN)
 gen_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# --------- Função de geração de texto -------------
+# ----------------- Função de geração de texto -----------------
 def generate_text(prompt, api_key):
     if not check_key(api_key):
         return "API Key inválida!"
     result = gen_pipe(prompt, max_length=100, do_sample=True)
     return result[0]["generated_text"]
 
-# --------- Interface Gradio -------------
+# ----------------- Interface Gradio -----------------
 with gr.Blocks() as demo:
-    gr.Markdown("## Gemma 270M API - Espaço Gratuito")
+    gr.Markdown("## Gemma 270M API - Hugging Face Space")
     
     with gr.Tab("Gerar Texto"):
         prompt_input = gr.Textbox(label="Prompt")