Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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from moviepy import VideoFileClip, TextClip, CompositeVideoClip
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from PIL import Image, ImageDraw, ImageFont
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import whisper
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from keybert import KeyBERT
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@@ -7,76 +7,75 @@ import numpy as np
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import warnings
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import os
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# Suppress Whisper FP16 warning
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warnings.filterwarnings("ignore", category=UserWarning, module="whisper")
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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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#
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def process_video(video_path, caption="Your Caption"):
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# Load
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clip = VideoFileClip(video_path)
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# ===
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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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try:
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pil_font = ImageFont.truetype(
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except:
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pil_font = ImageFont.load_default()
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draw.text((50, image.height - 100), caption, fill="white", font=pil_font)
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thumbnail_path = "thumbnail.jpg"
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image.save(thumbnail_path)
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# ===
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result = whisper_model.transcribe(video_path, language="hi")
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text = result["text"]
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# ===
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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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# ===
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try:
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size=(clip.w, None)
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)
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text_clip = text_clip.set_position(("center", "bottom")).set_duration(clip.duration)
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final_video = CompositeVideoClip([clip, text_clip])
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output_path = "output_with_caption.mp4"
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final_video.write_videofile(output_path, codec="libx264", audio_codec="aac", verbose=False, logger=None)
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return thumbnail_path, ", ".join(keywords_list), output_path
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# === Gradio
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with gr.Blocks() as demo:
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gr.Markdown("# πΉ Video
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with gr.Row():
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video_input = gr.File(label="π Upload Video", type="filepath")
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@@ -97,4 +96,3 @@ with gr.Blocks() as demo:
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)
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demo.launch(share=True)
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import gradio as gr
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from moviepy.editor import VideoFileClip, TextClip, CompositeVideoClip
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from PIL import Image, ImageDraw, ImageFont
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import whisper
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from keybert import KeyBERT
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import warnings
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import os
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# Suppress Whisper FP16 CPU warning
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warnings.filterwarnings("ignore", category=UserWarning, module="whisper")
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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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# Optional: Hindi-compatible system font (must be pre-installed or accessible by name)
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SYSTEM_FONT_NAME = "Noto-Sans-Devanagari" # This should be installed on the OS
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def process_video(video_path, caption="Your Caption"):
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# === 1. Load Video ===
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clip = VideoFileClip(video_path)
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# === 2. Extract Frame at 5s for Thumbnail ===
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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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# Use PIL to draw caption on thumbnail
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try:
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pil_font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoSansDevanagari-Regular.ttf", size=40)
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except:
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pil_font = ImageFont.load_default()
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draw.text((50, image.height - 100), caption, fill="white", font=pil_font)
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thumbnail_path = "thumbnail.jpg"
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image.save(thumbnail_path)
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# === 3. Transcribe Audio 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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# === 4. Extract SEO 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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# === 5. 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=SYSTEM_FONT_NAME, # Use font name (not file path)
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method='caption',
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size=(clip.w, None)
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)
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except Exception as e:
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print("Font fallback used due to error:", e)
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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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)
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text_clip = text_clip.set_position(("center", "bottom")).set_duration(clip.duration)
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final_video = CompositeVideoClip([clip, text_clip])
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output_path = "output_with_caption.mp4"
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final_video.write_videofile(output_path, codec="libx264", audio_codec="aac", verbose=False, logger=None)
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return thumbnail_path, ", ".join(keywords_list), output_path
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# === Gradio Interface ===
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with gr.Blocks() as demo:
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gr.Markdown("# πΉ Video Caption & SEO Tool (Hindi Supported)")
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with gr.Row():
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video_input = gr.File(label="π Upload Video", type="filepath")
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)
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demo.launch(share=True)
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