Create app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+from llama_cpp import Llama
+
+# Initialize the InferenceClient
+client = InferenceClient()
+
+llm = Llama.from_pretrained(
+    repo_id="mradermacher/OpenELM-1_1B-Instruct-GGUF",
+    filename="OpenELM-1_1B-Instruct.Q4_K_M.gguf",
+)
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    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 = ""
+
+    # Use the client to get the chat completion
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message['choices'][0]['delta']['content']
+        response += token
+        yield response
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly, conversational, helpful, and informative chatbot, designed to help users as best as possible. Responses should be fun to read, including the use of appropriate emojis in answers, wherever necesssary.", 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)",
+        ),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()