Update app.py

app.py

@@ -1,126 +1,45 @@
-import os
-import tempfile
-import uuid
-import shutil
-import warnings
-import logging
-
 import gradio as gr
-import yt_dlp
-import whisper
-from transformers import pipeline, MarianMTModel, MarianTokenizer
-
-# Suppress Whisper CPU warning
-warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead")
-
-# Setup logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Constants
-WHISPER_MODEL_SIZE = "small"
-SUMMARIZER_MODEL_NAME = "Falconsai/text_summarization"
-TRANSLATION_MODEL_NAME = "Helsinki-NLP/opus-mt-mul-en"
-COOKIES_PATH = "cookies.txt"
-
-# Load models once at startup
-whisper_model = whisper.load_model(WHISPER_MODEL_SIZE)
-summarizer = pipeline("summarization", model=SUMMARIZER_MODEL_NAME)
-translation_tokenizer = MarianTokenizer.from_pretrained(TRANSLATION_MODEL_NAME)
-translation_model = MarianMTModel.from_pretrained(TRANSLATION_MODEL_NAME)
-
-def save_cookies(file):
-    if file:
-        shutil.copy(file.name, COOKIES_PATH)
-        return "✅ Cookies uploaded successfully!"
-    return "⚠️ Please upload a valid cookies.txt file."
-
-def download_audio(youtube_url):
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".webm")
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'outtmpl': temp_file.name,
-        'quiet': True,
-    }
-    if os.path.exists(COOKIES_PATH):
-        ydl_opts['cookiefile'] = COOKIES_PATH
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([youtube_url])
-    return temp_file.name
-
-def get_thumbnail(youtube_url):
-    try:
-        ydl_opts = {'quiet': True}
-        if os.path.exists(COOKIES_PATH):
-            ydl_opts['cookiefile'] = COOKIES_PATH
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            info = ydl.extract_info(youtube_url, download=False)
-            return info.get("thumbnail", "")
-    except Exception as e:
-        logger.error(f"Thumbnail fetch error: {e}")
-        return ""
-
-def translate_to_english(text):
-    chunks = [text[i:i+500] for i in range(0, len(text), 500)]
-    translated = []
-    for chunk in chunks:
-        inputs = translation_tokenizer(chunk, return_tensors="pt", truncation=True, max_length=512)
-        output = translation_model.generate(**inputs, max_length=512)
-        translated.append(translation_tokenizer.decode(output[0], skip_special_tokens=True))
-    return " ".join(translated)
-
-def process_video(url):
-    try:
-        audio_path = download_audio(url)
-        result = whisper_model.transcribe(audio_path)
-        transcription = result["text"]
-
-        translated_text = translate_to_english(transcription)
-        summary = summarizer(translated_text, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
-        thumbnail_url = get_thumbnail(url)
-
-        os.remove(audio_path)  # Clean up temp audio file
-
-        return transcription, translated_text, summary, thumbnail_url
-    except Exception as e:
-        logger.exception("Error processing video")
-        return f"❌ Error: {str(e)}", "", "", ""
-
-def download_summary(text):
-    filename = os.path.join(tempfile.gettempdir(), f"summary_{uuid.uuid4().hex}.txt")
-    with open(filename, "w", encoding="utf-8") as f:
-        f.write(text)
-    return filename
-
-# Gradio UI
-with gr.Blocks(theme=gr.themes.Soft(), title="🎥 YouTube Video Summarizer") as demo:
-    gr.Markdown("## 🧠 Multilingual YouTube Summarizer")
-    gr.Markdown("Upload a video link and get the transcript, English translation, and summary.")
-
-    with gr.Row():
-        youtube_input = gr.Text(label="YouTube Video URL", placeholder="https://www.youtube.com/watch?v=...")
-        submit_btn = gr.Button("🎯 Transcribe & Summarize")
-
-    with gr.Row():
-        with gr.Column():
-            transcript_output = gr.Textbox(label="🔊 Original Transcript", lines=10)
-            translation_output = gr.Textbox(label="🌍 Translated to English", lines=10)
-            summary_output = gr.Textbox(label="🧾 Summary", lines=10)
-            download_btn = gr.Button("📥 Download Summary")
-            download_file = gr.File(label="Download Link")
-        video_thumb = gr.Image(label="🎞️ Video Thumbnail", width=256)
-
-    with gr.Row():
-        gr.Markdown("### 🔐 Upload `cookies.txt` (for YouTube access)")
-        cookies_file = gr.File(label="Upload cookies.txt", file_types=[".txt"])
-        cookie_status = gr.Textbox(label="Status", interactive=False)
-        upload_btn = gr.Button("📤 Upload Cookies")
-
-    # Button logic
-    submit_btn.click(
-        fn=process_video,
-        inputs=[youtube_input],
-        outputs=[transcript_output, translation_output, summary_output, video_thumb]
-    )
-
-    dow
+from transformers import pipeline
+import datetime
+
+# Initialize components
+note_generator = pipeline("text-generation", model="gpt2")
+notes_db = []
+
+def save_note(note):
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    notes_db.append({"text": note, "time": timestamp})
+    return f"Note saved at {timestamp}"
+
+def get_notes():
+    return "\n\n".join([f"{note['time']}:\n{note['text']}" for note in notes_db])
+
+def process_note(input_text, action):
+    if action == "Save":
+        return save_note(input_text)
+    elif action != "Default":
+        prompt = f"{action} this text: {input_text}\n\nResult:"
+        result = note_generator(prompt, max_length=200)[0]['generated_text']
+        return result
+    return input_text
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Advanced NoteGPT Clone")
+    with gr.Tab("Note Editor"):
+        with gr.Row():
+            with gr.Column():
+                user_input = gr.Textbox(label="Your Notes", lines=10)
+                action = gr.Radio(["Default", "Summarize", "Expand", "Rephrase", "Save"], 
+                                label="Action")
+                generate_btn = gr.Button("Process Note")
+            with gr.Column():
+                output = gr.Textbox(label="Result", lines=10)
+    
+    with gr.Tab("Saved Notes"):
+        notes_display = gr.Textbox(label="Your Saved Notes", lines=20)
+        refresh_btn = gr.Button("Refresh Notes")
+    
+    generate_btn.click(fn=process_note, inputs=[user_input, action], outputs=output)
+    refresh_btn.click(fn=get_notes, inputs=None, outputs=notes_display)
+
+demo.launch()