fix_doc

app.py

@@ -1,11 +1,12 @@
 import gradio as gr
 from recite_module import run
+from chatbot_module import respond
 demo = gr.Blocks()
 
 
 demo1 = gr.Interface(
     run,
-    [gr.Audio(sources=["microphone"] , type="numpy"), gr.Image(
+    [gr.Audio(sources=["microphone"], type="numpy"), gr.Image(
         type="filepath", label="Image")],
     gr.Image(type="pil", label="output Image"),
 )
@@ -15,8 +16,26 @@ demo2 = gr.Interface(
         type="filepath", label="Image")],
     [gr.Image(type="pil", label="output Image")]
 )
+demo3 = 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)",
+        ),
+    ],
+)
 with demo:
-    gr.TabbedInterface([demo1, demo2],
-                       ["Microphone", "Audio File"])
-
-demo.launch()
+    gr.TabbedInterface([demo1, demo2, demo3], [
+                       "Microphone", "Audio File", "Chatbot"])
+if __name__ == "__main__":
+    demo.launch()

chatbot_module.py

@@ -0,0 +1,63 @@
+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)",
+        ),
+    ],
+)
+
+
+if __name__ == "__main__":
+    demo.launch()

recite_module.py

@@ -258,4 +258,4 @@ def run(stream, image):
     if data2 is not None:
         return process_image(im_array, data2)
     else:
-        return im_array 
+        return im_array

requirements.txt

@@ -7,3 +7,4 @@ numpy
 torch
 librosa
 torchaudio
+huggingface_hub==0.22.2