Update app with ASR elements

Add transformer pipeline with Whisper large v3 to perform ASR elements, and added an ASR interface to provide audio inputs.

app.py

@@ -1,12 +1,19 @@
 import gradio as gr
+from transformers import pipeline
+
+# Use a pipeline as a high-level helper for automatic speech recognition
+pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
+
+# Define the function for ASR
+def transcribe(file):
+    result = pipe(file)["text"]
+    return result
+
+# Retain the ChatInterface setup from the existing app.py
 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]],
@@ -39,10 +46,10 @@ def respond(
         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(
+# Create two separate Gradio interfaces
+asr_interface = gr.Interface(fn=transcribe, inputs="file", outputs="text", title="ASR Transcription", description="Upload an audio file and get the transcription.")
+
+chat_interface = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
@@ -58,6 +65,12 @@ demo = gr.ChatInterface(
     ],
 )
 
+# Combine the two interfaces into a single Gradio Blocks application
+with gr.Blocks() as demo:
+    gr.Markdown("# ASR and Chatbot Application")
+    asr_interface.render()
+    gr.Markdown("----")
+    chat_interface.render()
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()