Update app.py

app.py

@@ -1,82 +1,92 @@
-# app.py
 import gradio as gr
 import requests
 from bs4 import BeautifulSoup
 import google.generativeai as genai
 import os
+import time
 
 # Configure Gemini API
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+if not GEMINI_API_KEY:
+    raise ValueError("GEMINI_API_KEY not found in environment variables")
+
 genai.configure(api_key=GEMINI_API_KEY)
 
 def fetch_article_content(url):
-    """Fetch article content using requests and BeautifulSoup"""
+    """Fetch article content with timeout and fallback"""
     try:
         headers = {'User-Agent': 'Mozilla/5.0'}
-        response = requests.get(url, headers=headers, timeout=10)
+        response = requests.get(url, headers=headers, timeout=8)  # 8s timeout
         response.raise_for_status()
         soup = BeautifulSoup(response.text, 'html.parser')
-
+        
         # Extract text from <p> tags
-        paragraphs = soup.find_all('p')
+        paragraphs = soup.find_all('p')[:10]  # Limit to 10 paragraphs
         content = ' '.join([p.get_text(strip=True) for p in paragraphs])
-        return content
+        return content[:2000]  # Limit to 2000 chars for prompt safety
     except Exception as e:
         return f"Error fetching article: {str(e)}"
 
 def generate_platform_post(article_text):
-    """Generate optimized post using Gemini API"""
+    """Generate optimized post using Gemini API with timeout"""
     try:
         model = genai.GenerativeModel('gemini-1.5-pro')
         
         prompt = f"""
-        Analyze this article content and create:
-        1. A compelling title (max 100 characters)
-        2. An optimized post in HTML format for Reddit/Quora
-        3. Include an image tag with descriptive alt text
+        Convert this article into a short, engaging post suitable for Reddit or Quora.
         
         Article content:
-        {article_text[:5000]}  # Limit to 5000 chars for token limits
+        {article_text}
         
-        Format your response as:
-        [TITLE]
-        [HTML_CONTENT]
+        Output format:
+        Title: [short title]
+        Post: [clean HTML with paragraphs and one image tag]
         
         Requirements:
-        - Clean HTML formatting with paragraphs
-        - Add relevant image tag with descriptive alt text
-        - Mobile-friendly design
-        - Minimal CSS styling
+        - Keep title under 100 characters
+        - Include one <img> tag with alt text
+        - Use minimal styling
+        - Mobile-friendly
+        - No markdown
         """
         
+        # Timeout mechanism
+        start_time = time.time()
         response = model.generate_content(prompt)
+        print(f"AI Response received in {time.time()-start_time:.2f} seconds")
+        
         return parse_gemini_response(response.text)
     except Exception as e:
-        return {"title": "Error generating post", "content": f"<p>{str(e)}</p>"}
+        return {"title": "Generation Failed", "content": f"<p>AI Error: {str(e)}</p>"}
 
 def parse_gemini_response(response):
-    """Parse Gemini's response into title and content"""
+    """Robust parsing with fallbacks"""
     try:
-        title = response.split("[TITLE]")[1].split("[HTML_CONTENT]")[0].strip()
-        content = response.split("[HTML_CONTENT]")[1].strip()
+        title = response.split("Title:")[1].split("Post:")[0].strip()[:100]
+        content = response.split("Post:")[1].strip()
     except:
         title = "Content Generation Error"
-        content = "<p>Failed to parse response from AI</p>"
+        content = "<p>Failed to parse AI response</p>"
     
     return {"title": title, "content": content}
 
 def process_url(url):
-    """Main processing pipeline"""
+    """Main processing pipeline with status updates"""
+    yield {"title": "Fetching Article...", "content": "<p>Connecting to URL...</p>"}
+    
     article_text = fetch_article_content(url)
     if article_text.startswith("Error"):
-        return {"title": "Processing Error", "content": f"<p>{article_text}</p>"}
+        yield {"title": "Fetch Error", "content": f"<p>{article_text}</p>"}
+        return
     
-    return generate_platform_post(article_text)
-
-# Create Gradio interface
-url_input = gr.Textbox(label="Article URL", placeholder="https://example.com/article...")
+    yield {"title": "Generating Post...", "content": "<p>Creating content with AI...</p>"}
+    
+    result = generate_platform_post(article_text)
+    yield result
+# Gradio Interface
+url_input = gr.Textbox(label="Article URL", placeholder="https://example.com/article...", lines=1)
 title_output = gr.Textbox(label="Generated Title")
-content_output = gr.HTML(label="Generated Post")
+content_output = gr.HTML(label="Formatted Post")
 
 app = gr.Interface(
     fn=process_url,
@@ -86,10 +96,12 @@ app = gr.Interface(
         gr.HTML(label="Formatted Post")
     ],
     examples=[
-        ["https://google.com"]
+        ["https://example.com/sample-article"]
     ],
     title="Article to Reddit/Quora Post Converter",
-    description="Convert news articles into optimized Reddit/Quora-style posts with AI-generated formatting and image descriptions"
+    description="Convert news articles into optimized posts with AI-generated formatting and image descriptions",
+    allow_flagging="never",
+    live=False
 )
 
 if __name__ == "__main__":