fr

app.py

@@ -1,8 +1,78 @@
-# Use a pipeline as a high-level helper
+import gradio as gr
+from huggingface_hub import InferenceClient
 from transformers import pipeline
 
-pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-14B")
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-pipe(messages)
+
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
+    """
+    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
+    """
+    client = InferenceClient(token=hf_token.token, model="deepseek-ai/DeepSeek-R1-Distill-Qwen-14B")
+
+    messages = [{"role": "system", "content": system_message}]
+
+    messages.extend(history)
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        choices = message.choices
+        token = ""
+        if len(choices) and choices[0].delta.content:
+            token = choices[0].delta.content
+
+        response += token
+        yield response
+
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+chatbot = gr.ChatInterface(
+    respond,
+    type="messages",
+    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)",
+        ),
+    ],
+)
+
+with gr.Blocks() as demo:
+    with gr.Sidebar():
+        gr.LoginButton()
+    chatbot.render()
+
+
+if __name__ == "__main__":
+    # Use a pipeline as a high-level helper
+
+    pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-14B")
+    messages = [
+        {"role": "user", "content": "Who are you?"},
+    ]
+    pipe(messages)
+    demo.launch()