initial commit

README.md

@@ -1,5 +1,5 @@
 ---
-title: Gradio Chatbot
+title: New1
 emoji: 💬
 colorFrom: yellow
 colorTo: purple
@@ -7,6 +7,8 @@ sdk: gradio
 sdk_version: 5.0.1
 app_file: app.py
 pinned: false
+license: apache-2.0
+short_description: generate tone accuracy
 ---
 
 An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -1,64 +1,25 @@
+import numpy as np
 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)",
-        ),
+notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
+
+def generate_tone(note, octave, duration):
+    sr = 48000
+    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
+    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
+    duration = int(duration)
+    audio = np.linspace(0, duration, duration * sr)
+    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
+    return sr, audio
+
+demo = gr.Interface(
+    generate_tone,
+    [
+        gr.Dropdown(notes, type="index"),
+        gr.Slider(4, 6, step=1),
+        gr.Textbox(value="1", label="Duration in seconds"),
     ],
+    "audio",
 )
-
-
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1 +1,2 @@
-huggingface_hub==0.25.2
+numpy
+requests