Update app.py

app.py

@@ -1,60 +1,47 @@
 import gradio as gr
+
 import os
-import requests
 from gradio_client import Client
 
-# Define the Hugging Face API URL and headers with your token
-API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
-HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")  # Replace with your actual token
-headers = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"}
-
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-
-def transcribe_and_summarize(youtube_url: str, task: str = "transcribe", return_timestamps: bool = False, summarize: bool = False, api_name: str = "/predict_2") -> dict:
+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.
+    Parameters:
+    - youtube_url (str): The YouTube URL to transcribe.
+    - task (str, optional): The task to perform. Default is "transcribe".
+    - return_timestamps (bool, optional): Whether to return timestamps. Default is True.
+    - api_name (str, optional): The API endpoint to use. Default is "/predict_2".
+    Returns:
+    - dict: The transcription result.
+    """
     client = Client("https://sanchit-gandhi-whisper-jax.hf.space/")
-    result = list(client.predict(youtube_url, task, return_timestamps, fn_index=7))
-    
-    # If the "summarize" checkbox is selected, summarize the transcription
-    if summarize:
-        transcription = result[1]
-        summary_result = query({"inputs": transcription})
-        try:
-            result[2] = summary_result[0]['summary_text']
-        except:
-            result[2] = 'Model is overloaded.'
-            
-    else:
-        result[2] = ''
-        
+    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=HyBw3wcZ124", "transcribe", False],
+    ["https://www.youtube.com/watch?v=H1YoNlz2LxA", "translate",False],
 ]
 
-# Define the Gradio interface with the "Summarize" checkbox and "Summary" output
+
 yt_transcribe = gr.Interface(
-    fn=transcribe_and_summarize,
+    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="Summarize")  # Added "Summarize" checkbox
-    ],
-    outputs=[
-        gr.outputs.HTML(label="Video"),
-        gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
-        gr.outputs.Textbox(label="Summary").style(show_copy_button=True)  # Added "Summary" output
+        gr.inputs.Checkbox(label="Return timestamps")
     ],
+    outputs=[gr.outputs.HTML(label="Video"),
+        gr.outputs.Textbox(label="Transcription").style(show_copy_button=True)],
     layout="horizontal",
     theme=gr.themes.Base(),
-    title="Whisper Large V2: Transcribe YouTube with Summarization",
+    title="Whisper Large V2: Transcribe YouTube",
     description=(
         "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
         f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
@@ -65,8 +52,8 @@ yt_transcribe = gr.Interface(
     cache_examples=False
 )
 
-with demo():
+with demo:
     gr.DuplicateButton()
-    gr.TabbedInterface([yt_transcribe], ["YouTube"])
+    gr.TabbedInterface([yt_transcribe], [ "YouTube"])
 
-demo.launch(enable_queue=True)
+demo.launch(enable_queue=True)