Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+"""import gradio as gr
 from huggingface_hub import InferenceClient
 
 
@@ -68,3 +68,124 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.launch()
+"""
+import gradio as gr
+import requests
+from huggingface_hub import InferenceClient
+
+DEEPGRAM_API_KEY = "YOUR_DEEPGRAM_API_KEY"
+
+def deepgram_stt(audio_file_path):
+    """
+    Send user microphone audio to Deepgram STT
+    """
+    url = "https://api.deepgram.com/v1/listen"
+    headers = {
+        "Authorization": f"Token {DEEPGRAM_API_KEY}",
+        "Content-Type": "audio/wav"
+    }
+
+    with open(audio_file_path, "rb") as f:
+        audio = f.read()
+
+    response = requests.post(url, headers=headers, data=audio).json()
+    return response["results"]["channels"][0]["alternatives"][0]["transcript"]
+
+
+def deepgram_tts(text):
+    """
+    Convert model output → speech using Deepgram TTS
+    """
+    url = "https://api.deepgram.com/v1/speak?model=aura-asteria-en"  # any model
+    headers = {
+        "Authorization": f"Token {DEEPGRAM_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {"text": text}
+
+    audio_out = "response.wav"
+    r = requests.post(url, json=payload, headers=headers)
+
+    with open(audio_out, "wb") as f:
+        f.write(r.content)
+
+    return audio_out
+
+
+def respond_audio(
+    audio_input,
+    history,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
+    """
+    STT → send to model → TTS
+    """
+    client = InferenceClient(
+        token=hf_token.token,
+        model="openai/gpt-oss-20b"
+    )
+
+    # ---- 1. Speech → text ----
+    user_message = deepgram_stt(audio_input)
+
+    messages = [{"role": "system", "content": system_message}]
+    messages.extend(history)
+    messages.append({"role": "user", "content": user_message})
+
+    # ---- 2. Model response ----
+    response_text = ""
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        if len(message.choices) and message.choices[0].delta.content:
+            response_text += message.choices[0].delta.content
+            yield response_text, None  # update text while streaming
+
+    # ---- 3. Text → audio ----
+    audio_file = deepgram_tts(response_text)
+
+    yield response_text, audio_file
+
+
+with gr.Blocks() as demo:
+    with gr.Sidebar():
+        gr.LoginButton()
+
+    gr.Markdown("## 🎤 Voice Chat Mode (Deepgram + GPT-OSS)")
+
+    # Hidden but expandable textbox
+    with gr.Accordion("Optional: Type Instead of Speaking", open=False):
+        typed_message = gr.Textbox(label="Manual Text Input")
+
+    chatbot = gr.Chatbot()
+
+    audio_in = gr.Audio(source="microphone", type="filepath", label="Press to Speak")
+    audio_out = gr.Audio(label="TTS Output")
+
+    system_message = gr.Textbox(
+        value="You are a friendly Chatbot.",
+        label="System message"
+    )
+    max_tokens = gr.Slider(1, 2048, value=512, label="Max new tokens")
+    temp = gr.Slider(0.1, 4.0, value=0.7, label="Temperature")
+    top_p = gr.Slider(0.1, 1.0, value=0.95, label="Top-p")
+
+    send_button = gr.Button("Send (Voice)")
+
+    send_button.click(
+        respond_audio,
+        inputs=[audio_in, chatbot, system_message, max_tokens, temp, top_p],
+        outputs=[chatbot, audio_out]
+    )
+
+if __name__ == "__main__":
+    demo.launch()