Delete app.py

app.py

@@ -1,93 +0,0 @@
-import os
-import yt_dlp
-import gradio as gr
-from faster_whisper import WhisperModel
-from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
-import openai
-import torch
-
-# Optional: Set your OpenAI API key (use env var for security)
-openai.api_key = "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"  # Replace with your real key or set as os.environ["OPENAI_API_KEY"]
-
-def download_and_extract_audio(youtube_url):
-    output_path = "downloads"
-    os.makedirs(output_path, exist_ok=True)
-
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'outtmpl': os.path.join(output_path, '%(id)s.%(ext)s'),
-        'postprocessors': [{
-            'key': 'FFmpegExtractAudio',
-            'preferredcodec': 'mp3',
-            'preferredquality': '192',
-        }],
-    }
-
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        info_dict = ydl.extract_info(youtube_url, download=True)
-        video_id = info_dict.get("id", None)
-        filename = os.path.join(output_path, f"{video_id}.mp3")
-        return filename
-
-def transcribe_audio(audio_path):
-    model = WhisperModel("base", compute_type="int8", device="cuda" if torch.cuda.is_available() else "cpu")
-    segments, _ = model.transcribe(audio_path)
-    transcript = " ".join([seg.text for seg in segments])
-    return transcript
-
-# Preload FLAN-T5 model offline
-tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
-model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
-local_gen = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
-
-def generate_response(transcript, user_prompt, use_online=False):
-    prompt = f"""You are a helpful AI assistant. Based on the transcript of a video, please {user_prompt.strip().lower()}.
-
-Transcript:
-{transcript[:3000]}"""
-
-    if use_online:
-        try:
-            response = openai.ChatCompletion.create(
-                model="gpt-4",
-                messages=[{"role": "user", "content": prompt}],
-                max_tokens=1000
-            )
-            return response.choices[0].message["content"]
-        except Exception as e:
-            return f"⚠️ Online API failed: {str(e)}"
-    else:
-        result = local_gen(prompt, max_length=1024, do_sample=False)
-        return result[0]['generated_text']
-
-def enhanced_ai_study_pipeline(video_source, youtube_url, upload_file, user_prompt, use_online_api):
-    try:
-        if video_source == "YouTube URL":
-            audio_path = download_and_extract_audio(youtube_url)
-        elif video_source == "Upload File" and upload_file is not None:
-            audio_path = upload_file.name
-        else:
-            return "No valid input provided.", ""
-
-        transcript = transcribe_audio(audio_path)
-        ai_response = generate_response(transcript, user_prompt, use_online=use_online_api)
-        return transcript, ai_response
-    except Exception as e:
-        return "Error occurred", str(e)
-
-video_input = gr.Radio(["YouTube URL", "Upload File"], label="Video Source")
-youtube_url = gr.Textbox(label="Enter YouTube URL")
-upload_file = gr.File(label="Upload a Video File", file_types=[".mp4", ".mp3", ".wav"])
-user_prompt = gr.Textbox(label="What do you want from the transcript?", placeholder="e.g., Prepare a diet plan based on this video")
-use_online_api = gr.Checkbox(label="Use Online API (GPT-4)", value=False)
-
-gr.Interface(
-    fn=enhanced_ai_study_pipeline,
-    inputs=[video_input, youtube_url, upload_file, user_prompt, use_online_api],
-    outputs=[
-        gr.Textbox(label="Transcript"),
-        gr.Textbox(label="AI Response")
-    ],
-    title="📚 AI Transcription Assistant (Offline + Online GPT)",
-    description="Upload or paste a YouTube video. Enter your goal and get a smart AI answer. Works offline with FLAN-T5 or online with GPT-4."
-).launch(share=True)