Autenticação com token

app.py

@@ -1,12 +1,14 @@
+import os
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+MODEL_ID = "unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF"
 
-#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-client = InferenceClient("unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF")
+# Pega o token do secret HF_TOKEN que você adicionou no Space
+token = os.environ.get("HF_TOKEN")
+
+# Inicializa o cliente; se token for None, InferenceClient tentará usar o token local/config.
+client = InferenceClient(model=MODEL_ID, token=token)
 
 def respond(
     message,
@@ -17,49 +19,65 @@ def respond(
     top_p,
 ):
     messages = [{"role": "system", "content": system_message}]
-
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-
     messages.append({"role": "user", "content": message})
 
-    response = ""
+    # MODE: escolha "stream_mode = True" para token por token, ou False para resposta completa de uma vez
+    stream_mode = True
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=False,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    if stream_mode:
+        response = ""
+        # stream=True entrega chunks — iteramos e extraímos 'content' do delta
+        for chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            # chunk pode ser dataclass/obj ou dict-like; tentamos extrair o texto com segurança
+            token_piece = ""
+            try:
+                delta = chunk.choices[0].delta
+                if isinstance(delta, dict):
+                    token_piece = delta.get("content", "") or ""
+                else:
+                    # objeto dataclass-like
+                    token_piece = getattr(delta, "content", "") or ""
+            except Exception:
+                # fallback genérico (caso a API retorne formato diferente)
+                token_piece = str(chunk)
 
-        response += token
-        yield response
+            response += token_piece
+            yield response
+
+    else:
+        # Sem streaming: recupera a resposta completa
+        completion = client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=False,
+            temperature=temperature,
+            top_p=top_p,
+        )
+        # conforme docs, a resposta completa aparece em:
+        text = completion.choices[0].message.content
+        yield text
 
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()