Update app.py

app.py

@@ -1,60 +1,63 @@
-import streamlit as st
-from streamlit_webrtc import webrtc_streamer, WebRtcMode, ClientSettings
-import av
-import numpy as np
-import wave
-import os
-
-st.title("Audio Recorder and Uploader")
-
-# Function to save recorded audio
-def save_audio(frames, filename, sample_width, framerate, nchannels):
-    wf = wave.open(filename, 'wb')
-    wf.setnchannels(nchannels)
-    wf.setsampwidth(sample_width)
-    wf.setframerate(framerate)
-    wf.writeframes(b''.join(frames))
-    wf.close()
-
-# Record audio
-if st.button("Start Recording"):
-    webrtc_ctx = webrtc_streamer(
-        key="key",
-        mode=WebRtcMode.SENDONLY,
-        client_settings=ClientSettings(
-            rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
-            media_stream_constraints={"audio": True, "video": False},
+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("HuggingFaceH4/zephyr-7b-beta")
+
+
+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 = ""
+
+    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
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", 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)",
         ),
-        audio_receiver_size=1024,
-    )
-
-    if webrtc_ctx.audio_receiver:
-        frames = []
-        sample_width = 2
-        framerate = 44100
-        nchannels = 1
-
-        while True:
-            try:
-                audio_frames = webrtc_ctx.audio_receiver.get_frames(timeout=1)
-                for frame in audio_frames:
-                    sound_chunk = np.frombuffer(frame.to_ndarray(), dtype=np.int16)
-                    frames.append(sound_chunk.tobytes())
-            except av.error.BlockingIOError:
-                break
-
-        filename = "recorded_audio.wav"
-        save_audio(frames, filename, sample_width, framerate, nchannels)
-        st.audio(filename, format='audio/wav')
-
-# Upload audio
-uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3"])
-
-if uploaded_file is not None:
-    st.audio(uploaded_file, format='audio/wav')
-    file_details = {"filename": uploaded_file.name, "filetype": uploaded_file.type,
-                    "filesize": uploaded_file.size}
-    st.write(file_details)
-    with open(os.path.join("uploads", uploaded_file.name), "wb") as f:
-        f.write(uploaded_file.getbuffer())
-    st.success("File uploaded successfully")
+    ],
+)
+
+
+if __name__ == "__main__":
+    demo.launch()