Importing files from HF Spaces

README.md

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+---
+title: CSDS553 Demo
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 5.42.0
+app_file: app.py
+pinned: false
+hf_oauth: true
+hf_oauth_scopes:
+- inference-api
+---
+
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
+
+pipe = None
+stop_inference = False
+
+# Fancy styling
+fancy_css = """
+#main-container {
+    background-color: #f0f0f0;
+    font-family: 'Arial', sans-serif;
+}
+.gradio-container {
+    max-width: 700px;
+    margin: 0 auto;
+    padding: 20px;
+    background: white;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    border-radius: 10px;
+}
+.gr-button {
+    background-color: #4CAF50;
+    color: white;
+    border: none;
+    border-radius: 5px;
+    padding: 10px 20px;
+    cursor: pointer;
+    transition: background-color 0.3s ease;
+}
+.gr-button:hover {
+    background-color: #45a049;
+}
+.gr-slider input {
+    color: #4CAF50;
+}
+.gr-chat {
+    font-size: 16px;
+}
+#title {
+    text-align: center;
+    font-size: 2em;
+    margin-bottom: 20px;
+    color: #333;
+}
+"""
+
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+    use_local_model: bool,
+):
+    global pipe
+
+    # Build messages from history
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend(history)
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    if use_local_model:
+        print("[MODE] local")
+        from transformers import pipeline
+        import torch
+        if pipe is None:
+            pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct")
+
+        # Build prompt as plain text
+        prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages])
+
+        outputs = pipe(
+            prompt,
+            max_new_tokens=max_tokens,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+        )
+
+        response = outputs[0]["generated_text"][len(prompt):]
+        yield response.strip()
+
+    else:
+        print("[MODE] api")
+
+        if hf_token is None or not getattr(hf_token, "token", None):
+            yield "⚠️ Please log in with your Hugging Face account first."
+            return
+
+        client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+
+        for chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            choices = chunk.choices
+            token = ""
+            if len(choices) and choices[0].delta.content:
+                token = choices[0].delta.content
+            response += token
+            yield response
+
+
+chatbot = gr.ChatInterface(
+    fn=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=2.0, 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.Checkbox(label="Use Local Model", value=False),
+    ],
+    type="messages",
+)
+
+with gr.Blocks(css=fancy_css) as demo:
+    with gr.Row():
+        gr.Markdown("<h1 style='text-align: center;'>🌟 Fancy AI Chatbot 🌟</h1>")
+        gr.LoginButton()
+    chatbot.render()
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch