Update app.py
Browse files
app.py
CHANGED
@@ -6,59 +6,76 @@ from keybert import KeyBERT
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import numpy as np
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import os
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# Load models
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whisper_model = whisper.load_model("base")
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kw_model = KeyBERT()
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def process_video(video_path, caption="Your Caption"):
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# Load video
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clip = VideoFileClip(video_path)
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# Extract
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frame = clip.get_frame(5)
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image = Image.fromarray(np.uint8(frame))
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draw = ImageDraw.Draw(image)
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thumbnail_path = "thumbnail.jpg"
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image.save(thumbnail_path)
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# Transcribe Hindi
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result = whisper_model.transcribe(video_path, language="hi")
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text = result["text"]
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# Extract keywords
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keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 2), stop_words='english')
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keywords_list = [kw[0] for kw in keywords]
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# Burn caption
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output_path = "output_with_caption.mp4"
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final.write_videofile(output_path, codec=
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return thumbnail_path, ", ".join(keywords_list), output_path
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# Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("# Video Thumbnail
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video_input = gr.File(label="Upload Video", type="filepath")
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caption_input = gr.Textbox(label="
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generate_button = gr.Button("Generate Thumbnail &
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thumbnail_output = gr.Image(label="Generated Thumbnail")
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keywords_output = gr.Textbox(label="SEO Keywords")
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video_output = gr.File(label="Download
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generate_button.click(
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fn=process_video,
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@@ -66,4 +83,4 @@ with gr.Blocks() as demo:
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outputs=[thumbnail_output, keywords_output, video_output]
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)
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demo.launch()
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import numpy as np
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import os
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# Load Whisper and KeyBERT models
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whisper_model = whisper.load_model("base")
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kw_model = KeyBERT()
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# Path to Hindi-supporting font (adjust if needed)
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FONT_PATH = "/usr/share/fonts/truetype/noto/NotoSansDevanagari-Regular.ttf" # Use system font or bundle locally
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def process_video(video_path, caption="Your Caption"):
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# Load the video
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clip = VideoFileClip(video_path)
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# Extract frame at 5 seconds
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frame = clip.get_frame(5)
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image = Image.fromarray(np.uint8(frame))
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draw = ImageDraw.Draw(image)
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# Load PIL font (for thumbnail)
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try:
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font = ImageFont.truetype(FONT_PATH, size=40)
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except:
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font = ImageFont.load_default()
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draw.text((50, image.height - 100), caption, fill="white", font=font)
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thumbnail_path = "thumbnail.jpg"
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image.save(thumbnail_path)
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# Transcribe audio with Whisper in Hindi
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result = whisper_model.transcribe(video_path, language="hi")
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text = result["text"]
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# Extract keywords using KeyBERT
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keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 2), stop_words='english')
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keywords_list = [kw[0] for kw in keywords]
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# Burn caption onto video
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try:
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text_clip = TextClip(
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caption,
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fontsize=50,
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color='white',
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font=FONT_PATH,
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method='caption',
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size=(clip.w, None)
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).set_position(("center", "bottom")).set_duration(clip.duration)
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except:
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# fallback in case font doesn't support Hindi
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text_clip = TextClip(
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caption,
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fontsize=50,
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color='white',
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method='caption',
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size=(clip.w, None)
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).set_position(("center", "bottom")).set_duration(clip.duration)
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final = CompositeVideoClip([clip, text_clip])
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output_path = "output_with_caption.mp4"
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final.write_videofile(output_path, codec="libx264", audio_codec="aac")
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return thumbnail_path, ", ".join(keywords_list), output_path
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# Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("# 📹 Video Thumbnail & SEO Tool (Hindi Supported)")
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video_input = gr.File(label="📁 Upload Video", type="filepath")
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caption_input = gr.Textbox(label="📝 Caption for Thumbnail/Video", value="यह शानदार वीडियो है!")
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generate_button = gr.Button("🚀 Generate Thumbnail, SEO & Video")
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thumbnail_output = gr.Image(label="🖼️ Generated Thumbnail")
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keywords_output = gr.Textbox(label="🔑 Extracted SEO Keywords")
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video_output = gr.File(label="⬇️ Download Final Video")
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generate_button.click(
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fn=process_video,
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outputs=[thumbnail_output, keywords_output, video_output]
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)
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demo.launch(share=True) # share=True creates public link
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