Create app.py

app.py

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+import os
+import edge_tts
+import tempfile
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+"""
+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("google/gemma-3-27b-it", token=os.getenv("TOKEN"))
+
+# client = InferenceClient(
+#     provider="fireworks-ai",
+#     api_key=os.getenv("TOKEN"),
+# )
+
+global history
+history = []
+async def respond(
+    message, 
+    history=[],
+    system_message="You are a DorjGPT, created by Dorjzodovsuren. You is a helpful assistant and always reply back in Mongolian, and only return Mongolian text within 50 words.",
+    max_tokens=512,
+    temperature=0.001,
+    top_p=0.95,
+):
+    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 = ""
+
+    for message in client.chat_completion(
+        model="google/gemma-3-27b-it",
+        messages=messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+        response += token
+
+    # completion = client.chat.completions.create(
+    #     model="deepseek-ai/DeepSeek-R1",
+    #     messages=messages,
+    #     max_tokens=500,
+    # )
+    # response = completion.choices[0].message.content
+    # print(response)
+        
+    communicate = edge_tts.Communicate(response, voice="mn-MN-YesuiNeural")
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    yield tmp_path
+
+
+with gr.Blocks(theme="gradio/monochrome", title="Dorj Assistant") as demo:
+    gr.HTML("""
+        <h1 style="text-align: center; style="font-size: 3m;">
+        DorjGPT
+        </h1>
+        """)
+    with gr.Column():
+        output_audio = gr.Audio(label="DorjGPT", type="filepath",
+                  interactive=True,
+                  visible=True,
+                  autoplay=True,
+                  elem_classes="audio")
+        
+        user_input = gr.Textbox(label="Question", value="What is this application?")
+
+    with gr.Tab():
+      with gr.Row():
+        translate_btn = gr.Button("Submit")
+        translate_btn.click(fn=respond, inputs=user_input,
+                            outputs=output_audio, api_name="translate")  
+
+if __name__ == "__main__":
+    demo.queue(max_size=30).launch()