Update app.py

app.py

@@ -2,59 +2,49 @@ import gradio as gr
 import os
 from langchain_openai import ChatOpenAI
 
-# Set up the SambaNova Chat API client
 api_key = os.environ.get("FEATHERLESS_API_KEY")
-llm = ChatOpenAI(
-    base_url="https://api.featherless.ai/v1/",
-    api_key=api_key,
-    streaming=True,
-    model="mistralai/Magistral-Small-2506",
-)
 
-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})
-
-    # Update the ChatOpenAI instance with the current parameters
+MODEL_CHOICES = [
+    "Qwen/Qwen3-32B",
+    "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+    "meta-llama/Llama-3.3-70B-Instruct",
+    "mistralai/Magistral-Small-2506",
+    "unsloth/DeepSeek-R1-Distill-Llama-70B",
+    "unsloth/Qwen2.5-72B-Instruct",
+    "unsloth/Llama-3.3-70B-Instruct",
+]
+
+def create_llm(model_name: str):
+    return ChatOpenAI(
+        base_url="https://api.featherless.ai/v1/",
+        api_key=api_key,
+        streaming=True,
+        model=model_name,
+    )
+
+def respond(message, history, system_message, max_tokens, temperature, top_p, model_name):
+    llm = create_llm(model_name)
+    messages = [{"role":"system","content":system_message}]
+    for u, a in history:
+        if u: messages.append({"role":"user","content":u})
+        if a: messages.append({"role":"assistant","content":a})
+    messages.append({"role":"user","content":message})
     llm.max_tokens = max_tokens
     llm.temperature = temperature
     llm.top_p = top_p
-
     response = ""
     for chunk in llm.stream(messages):
-        token = chunk.content
-        response += token
+        response += chunk.content
         yield response
 
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=16384, value=2048, 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)",
-        ),
-    ],
-)
+with gr.Blocks() as demo:
+    with gr.Row():
+        model_dropdown = gr.Dropdown(choices=MODEL_CHOICES, value=MODEL_CHOICES[0], label="Pilih model")
+    chatbot = gr.Chatbot()
+    system_msg = gr.Textbox("You are a friendly Chatbot.", label="System message")
+    max_t = gr.Slider(1, 16384, value=2048, step=1, label="Max new tokens")
+    temp = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
+    top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top‑p")
 
-if __name__ == "__main__":
+    model_dropdown.change(lambda x: None, inputs=model_dropdown, outputs=[])
     demo.launch()