Update app.py

app.py

@@ -6,19 +6,18 @@ from transformers import pipeline
 from gtts import gTTS
 from bs4 import BeautifulSoup
 import tempfile
-import os
 import requests
-from PIL import Image, ImageDraw, ImageFont
 import subprocess
 import concurrent.futures
+from PIL import Image, ImageDraw
 
 # CPU-friendly summarization model
 summary_pipe = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
 llm = HuggingFacePipeline(pipeline=summary_pipe)
 
-# LangChain summarization prompt (short summary)
+# Prompt for <50-word promotional summary
 summary_prompt = PromptTemplate.from_template("""
-Give a crisp and short summary of the following content (under 50 words):
+Provide a crisp, promotional-style summary (under 50 words) of the following:
 
 {text}
 
@@ -26,85 +25,74 @@ Summary:
 """)
 summary_chain = LLMChain(llm=llm, prompt=summary_prompt)
 
+# Extract main article content
 def extract_main_content(url):
-    try:
-        response = requests.get(url, timeout=10)
-        soup = BeautifulSoup(response.content, "html.parser")
-        for tag in soup(["nav", "header", "footer", "aside", "script", "style", "noscript"]):
-            tag.decompose()
-        paragraphs = soup.find_all("p")
-        content = "\n".join([p.get_text() for p in paragraphs if len(p.get_text()) > 60])
-        return content.strip()
-    except Exception as e:
-        return f"Error extracting article content: {str(e)}"
-
-def create_text_image(summary_text, image_path):
-    img = Image.new("RGB", (1280, 720), color=(0, 0, 0))
+    resp = requests.get(url, timeout=10)
+    soup = BeautifulSoup(resp.content, "html.parser")
+    for tag in soup(["nav","header","footer","aside","script","style","noscript"]): tag.decompose()
+    paras = soup.find_all("p")
+    content = "\n".join(p.get_text() for p in paras if len(p.get_text())>60)
+    return content or None
+
+# Create gradient background image
+def create_background(image_path, size=(1280,720)):
+    img = Image.new("RGB", size)
     draw = ImageDraw.Draw(img)
-    font = ImageFont.load_default()
-    wrapped = summary_text[:512] + ('...' if len(summary_text) > 512 else '')
-    draw.text((50, 50), wrapped, fill=(255, 255, 255), font=font)
+    for i in range(size[1]):
+        # gradient from dark blue to black
+        r = int(10 + (i/size[1])*20)
+        g = int(20 + (i/size[1])*30)
+        b = int(50 + (i/size[1])*50)
+        draw.line([(0, i), (size[0], i)], fill=(r, g, b))
     img.save(image_path)
 
-def generate_video(image_path, audio_path, output_path):
+# Generate AV summary
+def url_to_av_summary(url):
+    text = extract_main_content(url)
+    if not text:
+        return "Failed to extract article content.", None
+    text = text[:3000]
+    summary = summary_chain.run(text=text)
+    summary = summary.replace('"','')
+    # TTS
+    audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
+    gTTS(text=summary).save(audio_path)
+    # Background image
+    bg_path = tempfile.NamedTemporaryFile(delete=False, suffix=".png").name
+    create_background(bg_path)
+    # Video with animated text
+    video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
     cmd = [
-        "ffmpeg", "-y",
-        "-loop", "1",
-        "-i", image_path,
-        "-i", audio_path,
-        "-t", "15",
-        "-c:v", "libx264",
-        "-tune", "stillimage",
-        "-c:a", "aac",
-        "-b:a", "192k",
-        "-pix_fmt", "yuv420p",
-        "-shortest",
-        output_path
+        'ffmpeg', '-y',
+        '-loop', '1', '-i', bg_path,
+        '-i', audio_path,
+        '-vf', (
+            "drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text='" + summary +
+            "':fontcolor=white:fontsize=48:box=1:[email protected]:boxborderw=5:"
+            "x=(w-text_w)/2:y=h-(t*(h+text_h)/15)"
+        ),
+        '-t', '15',
+        '-c:v', 'libx264', '-c:a', 'aac', '-pix_fmt', 'yuv420p', '-shortest', video_path
     ]
     subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+    return summary, video_path
 
-def url_to_av_summary(url):
-    try:
-        article_text = extract_main_content(url)
-        if article_text.startswith("Error"):
-            return article_text, None
-
-        article_text = article_text[:3000]  # Further truncated
-        summary = summary_chain.run(text=article_text)
-
-        tts = gTTS(text=summary)
-        audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
-        tts.save(audio_path)
-
-        image_path = tempfile.NamedTemporaryFile(delete=False, suffix=".png").name
-        video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-
-        create_text_image(summary, image_path)
-        generate_video(image_path, audio_path, video_path)
-
-        return summary, video_path
-
-    except Exception as e:
-        return f"Error: {str(e)}", None
-
-def safe_summary_with_timeout(url, timeout_secs=60):
+# Timeout wrapper
+def safe_summary(url, timeout_secs=60):
     with concurrent.futures.ThreadPoolExecutor() as executor:
         future = executor.submit(url_to_av_summary, url)
         try:
             return future.result(timeout=timeout_secs)
         except concurrent.futures.TimeoutError:
-            return "⏱️ Processing took too long. Try a shorter article.", None
+            return "⏱️ Processing timed out.", None
 
 iface = gr.Interface(
-    fn=safe_summary_with_timeout,
-    inputs=gr.Textbox(label="Article URL", placeholder="Paste a news/blog URL here..."),
-    outputs=[
-        gr.Textbox(label="Summary"),
-        gr.Video(label="Video Summary")
-    ],
-    title="🧠 Short AV Summary from URL",
-    description="Extracts clean article content and creates a <15 second narrated video with a short crisp summary. 100% CPU-compatible."
+    fn=safe_summary,
+    inputs=gr.Textbox(label="Article URL"),
+    outputs=[gr.Textbox(label="Summary"), gr.Video(label="Video Preview")],
+    title="🎥 Promo-Style AV Summary with Gradient Background",
+    description="Generates a <15s video with animated text over a gradient background. CPU-only, HuggingFace Spaces-ready."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+if __name__ == '__main__':
+    iface.launch()