fixed

app.py

@@ -1,35 +1,7 @@
-# ########## Overview ################################################################
-#  The application is divided into three main components:
-#  Extracts metadata, transcripts, and thumbnails from YouTube videos.
-#  Summarizes the extracted transcripts using the OpenAI API.
-#  Builds a Streamlit web interface that ties everything together.
-#
-#Step 1: Extracting YouTube Data
-#	The first step involves extracting the video ID, metadata (title and channel name), 
-# 	transcript, and thumbnail image from a YouTube URL. 
-#
-#Step 2: Summarizing the Transcript
-#	With the transcript in hand, the next step is to summarize it. This is done using 
-# 	the OpenAI API in summarize_text.py. Before running this script, make sure to set 
-# 	your OpenAI API key as an environment variable:
-#
-#Step 3: Building the Web Interface
-#	Finally, the web interface is created using Streamlit. Streamlit is a Python library 
-# 	that simplifies the process of creating web applications for data science and machine 
-#   learning projects. This file combines the functionality of the other two scripts and 
-# 	provides a user-friendly interface.
-#Bringing It All Together
-#	Extract Video ID and Metadata: When a user inputs a YouTube URL, the app extracts the 
-# 	video ID and retrieves the title and channel name using extract_metadata(url).
-#	Download Thumbnail: The app downloads the video’s thumbnail and displays it using 
-# 	get_thumbnail_from_url(url).
-#	Get and Summarize Transcript: The transcript is fetched using get_transcript_from_url(url) 
-# 	and summarized using summarize_transcript(transcript, language). The summary is displayed 
-# 	in the selected language.
-#################################################################################################
-# TO RUN, execute on the terminal:
-#streamlit run C:\projects\ewp\ai-projects\ewp-sales-bots-repo\ewp-sales-bots-program\notebooks\summarize_mp4_video.py
-#
+#####################################################################
+#This application provides and esample of FASTAPI for
+# transcribing an mp4 video and summarizing the transcription
+######################################################################
 
 import os
 import subprocess
@@ -56,14 +28,13 @@ app=FastAPI()
 # Initialize the text generation pipeline
 #pipe = pipeline("text2text-generation", model="google/flan-t5-small")
 
-
+# This is default function
 @app.get("/")
 def home():
     return {"message":"Hello World"}
 
 # Define a function to handle the GET request at `/generate`
-
-
+# 
 @app.get("/generate")
 def generate(text:str):
     ## use the pipeline to generate text from given input text
@@ -72,6 +43,12 @@ def generate(text:str):
     ## return the generate text in Json reposne
     return {"output":output[0]['generated_text']}
 
+# This function
+# 1. takes mp4 video as input 
+# 2. creates mp3 file from mp4 file
+# 3. creates wav file from mp3 file
+# 4. transcribes to text from the wav file
+
 @app.get("/transcribe")
 def get_transcript_mp4(mp4_path):
     #path_to_check = r"C:\Program Files\ffmpeg\bin"
@@ -117,7 +94,11 @@ def get_transcript_mp4(mp4_path):
     else:
         print("File NOT found! Check the path for: ", mp4_path)       
         return "Test summary"
-    
+
+# This function
+# 1. takes text as input 
+# 2. summarizes the input text
+
 @app.get("/summarize")
 def summarize_text(text, lang='en'):
     OpenAI.api_key = os.getenv("OPENAI_API_KEY")
@@ -150,155 +131,3 @@ def summarize_text(text, lang='en'):
     return summary_text
 
 
-import torch
-
-import gradio as gr
-import yt_dlp as youtube_dl
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-import tempfile
-import os
-
-MODEL_NAME = "openai/whisper-large-v3"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-
-def transcribe(inputs, task):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return  text
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
-    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
-
-def yt_transcribe(yt_url, task, max_filesize=75.0):
-    html_embed_str = _return_yt_html_embed(yt_url)
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        filepath = os.path.join(tmpdirname, "video.mp4")
-        download_yt_audio(yt_url, filepath)
-        with open(filepath, "rb") as f:
-            inputs = f.read()
-
-    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
-    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-
-    return html_embed_str, text
-
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        #gr.Audio(source="microphone", type="filepath", optional=True),
-        gr.Audio(sources="microphone", type="filepath"),
-        gr.Radio(["transcribe", "translate"], label="Task"),
-    ],
-    outputs="text",
-    #layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-file_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.Audio(sources="upload", type="filepath", label="Audio file"),
-        gr.Radio(["transcribe", "translate"], label="Task"),
-    ],
-    outputs="text",
-    #layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.Radio(["transcribe", "translate"], label="Task")
-    ],
-    outputs=["html", "text"],
-    #layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe YouTube",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
-
-#demo.launch(enable_queue=True)
-demo.launch()