Update app.py

app.py

@@ -1,35 +1,8 @@
 import gradio as gr
-import requests
-import os
-
-# Define the Whisper ASR function (transcribe_audio) here
-
-# Retrieve the API token from the environment variable
-API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
-
-# Check if the API token is available
-if not API_TOKEN:
-    raise ValueError("HUGGINGFACE_API_TOKEN environment variable is not set.")
 
-# Define the BART summarization API URL
-API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
-HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
-
-def query(payload):
-    response = requests.post(API_URL, headers=HEADERS, json=payload)
-    return response.json()
+import os
+from gradio_client import Client
 
-def summarize_video(youtube_url: str, task: str = "transcribe", return_timestamps: bool = False) -> dict:
-    # Call your transcribe_audio function to get the transcription
-    transcription_result = transcribe_audio(youtube_url, task, return_timestamps)
-    
-    # Summarize the transcription
-    summary_result = query({
-        "inputs": transcription_result["transcription"]
-    })
-    
-    return summary_result[0]['summary_text']
-    
 def transcribe_audio(youtube_url: str, task: str = "transcribe", return_timestamps: bool = False, api_name: str = "/predict_2") -> dict:
     """
     Transcribe audio from a given YouTube URL using a specified model.
@@ -45,19 +18,24 @@ def transcribe_audio(youtube_url: str, task: str = "transcribe", return_timestam
     result = client.predict(youtube_url, task, return_timestamps, fn_index=7)
     return result
 
+
+
 MODEL_NAME = "openai/whisper-large-v2"
 
+
+demo = gr.Blocks()
+
 EXAMPLES = [
-    ["https://www.youtube.com/watch?v=H1YoNlz2LxA", "translate", False],
+    ["https://www.youtube.com/watch?v=H1YoNlz2LxA", "translate",False],
 ]
 
-# Define the Gradio interface for transcription
+
 yt_transcribe = gr.Interface(
     fn=transcribe_audio,
     inputs=[
         gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
         gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(label="Return timestamps"),
+        gr.inputs.Checkbox(label="Return timestamps")
     ],
     outputs=[gr.outputs.HTML(label="Video"),
         gr.outputs.Textbox(label="Transcription").style(show_copy_button=True)],
@@ -74,32 +52,8 @@ yt_transcribe = gr.Interface(
     cache_examples=False
 )
 
-# Define the Gradio interface for summarization
-yt_summarize = gr.Interface(
-    fn=summarize_video,
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(label="Return timestamps")
-    ],
-    outputs=[gr.outputs.HTML(label="Video"),
-        gr.outputs.Textbox(label="Summary").style(show_copy_button=True)],
-    layout="horizontal",
-    theme=gr.themes.Base(),
-    title="Whisper Large V2: Summarize YouTube",
-    description=(
-        "Summarize long-form YouTube videos with the click of a button! This tab uses the Whisper ASR model for transcription"
-        " and BART for summarization."
-    ),
-    allow_flagging="never",
-    examples=EXAMPLES,
-    cache_examples=False
-)
-
-# Add the "Summarize" tab to the Gradio interface
-
-# Launch the Gradio interface
-with yt_transcribe:
+with demo:
     gr.DuplicateButton()
-    gr.TabbedInterface([yt_transcribe,yt_summarize], ["Transcribe","Summarize"])
-    yt_transcribe.launch(enable_queue=True)
+    gr.TabbedInterface([yt_transcribe], [ "YouTube"])
+
+demo.launch(enable_queue=True)