Update app.py

app.py

@@ -1,44 +1,48 @@
-
 import gradio as gr
-
+import requests
 import os
-from gradio_client import Client
-
-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".
+# Define the Whisper ASR function (transcribe_audio) here
 
-    Returns:
-    - dict: The transcription result.
-    """
-    client = Client("https://sanchit-gandhi-whisper-jax.hf.space/")
-    result = client.predict(youtube_url, task, return_timestamps, fn_index=7)
-    return result
+# 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}"}
 
-MODEL_NAME = "openai/whisper-large-v2"
+def query(payload):
+    response = requests.post(API_URL, headers=HEADERS, json=payload)
+    return response.json()
 
+def summarize_video(youtube_url: str, task: str = "transcribe", return_timestamps: bool = False, summary_length: int = 150) -> 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"][:summary_length]
+    })
+    
+    return summary_result
 
-demo = gr.Blocks()
+MODEL_NAME = "openai/whisper-large-v2"
 
 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)],
@@ -55,9 +59,34 @@ yt_transcribe = gr.Interface(
     cache_examples=False
 )
 
-with demo:
-    gr.DuplicateButton()
-    gr.TabbedInterface([yt_transcribe], [ "YouTube"])
+# 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"),
+        gr.inputs.Number(default=150, label="Summary Length", min=1, max=500),
+    ],
+    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
+)
 
-demo.launch(enable_queue=True)
+# Add the "Summarize" tab to the Gradio interface
+yt_transcribe.tabs["Summarize"] = yt_summarize
 
+# Launch the Gradio interface
+with yt_transcribe:
+    gr.DuplicateButton()
+    gr.TabbedInterface([yt_transcribe], ["YouTube"])
+    yt_transcribe.launch(enable_queue=True)