Deploy from anycoder

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the client with a lightweight, capable model
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+def respond(message, history):
+    """
+    Formats the chat history and streams the response from the LLM.
+    """
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    
+    # Convert Gradio history to OpenAI format
+    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})
+
+    # Stream response
+    response = ""
+    for msg in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7):
+        token = msg.choices[0].delta.content
+        if token:
+            response += token
+            yield response
+
+# Build the Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("### [Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)")
+    
+    gr.ChatInterface(
+        respond,
+        title="50-Line Chatbot",
+        description="A streaming AI assistant built with Gradio and Hugging Face Inference API.",
+        examples=["Tell me a joke", "Write a python script", "What is the capital of France?"]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0