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.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "main_page.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run main_page.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

README.md

@@ -1,12 +1,85 @@
----
-title: Latest Ai News
-emoji: 📉
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Latest AI News
+
+This repository serves as a curated collection of the most recent and significant developments in artificial intelligence. The goal is to provide AI enthusiasts, researchers, students, and professionals with a centralized resource to stay updated on breakthroughs, research papers, product launches, and industry trends.
+
+## 📚 Contents
+
+This repository includes:
+- News about major AI product releases
+- Updates on AI industry trends
+- Insights from AI conferences and events
+- Resources for learning about new AI technologies
+
+## 🔗 App Link
+
+Check out the live application: https://latest-ai-news.streamlit.app/
+
+## 🎬 Demo
+
+Watch our video demonstration to see the app in action:
+
+
+https://github.com/user-attachments/assets/1db2065e-766a-47f5-b23a-56aad8938841
+
+
+## ✨ Features
+
+- **Modern Card-Based Interface**: News displayed in an elegant three-column grid layout
+- **Responsive Design**: Works well on different screen sizes
+- **RSS Feed Integration**: Automatically pulls content from top AI news sources
+- **Smart Filtering**: Filter news by date range and source
+- **Customizable View**: Select specific news sources or view all content
+- **Real-time Updates**: Refresh data with a single click
+- **Image Support**: Displays source images when available
+
+## 🔄 Updates
+
+This repository is updated regularly to ensure the content remains current and relevant. Check back frequently or watch the repository to be notified of new updates.
+
+## 💻 Technical Details
+
+- **Framework**: Built with Streamlit for rapid development and easy deployment
+- **Data Sources**: Aggregates news from 12+ leading AI publications including:
+  - Google DeepMind Blog
+  - OpenAI News
+  - MIT Technology Review
+  - NVIDIA Blog
+  - Microsoft Research
+  - The Berkeley AI Research Blog
+  - And more...
+- **Automatic Content Processing**: Handles different date formats and content structures
+- **Parallel Processing**: Uses concurrent fetching for faster data retrieval
+
+## 🤝 Contributing
+
+Contributions are welcome! If you'd like to contribute to this repository:
+
+1. Fork the repository
+2. Create a new branch (`git checkout -b feature/your-feature`)
+3. Commit your changes (`git commit -m 'Add some feature'`)
+4. Push to the branch (`git push origin feature/your-feature`)
+5. Open a Pull Request
+
+### Contribution Guidelines
+- Ensure all information is accurate and from reliable sources
+- Include references or links to original sources
+- Follow the existing formatting structure
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 📞 Contact
+
+If you have any questions or suggestions, feel free to:
+- Open an issue in this repository
+- Contact the repository owner (Buse Koseoglu) through GitHub
+
+## ⭐ Star This Repository
+
+If you find this repository useful, please consider giving it a star to help others discover it!
+
+---
+
+Created and maintained by [Buse Koseoglu](https://github.com/busekoseoglu)

fetch_data.py

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+import feedparser
+import pandas as pd
+from datetime import datetime, timedelta
+import ssl
+from bs4 import BeautifulSoup
+import warnings
+import concurrent.futures
+import re
+import requests
+
+warnings.filterwarnings("ignore")
+
+URL = "https://www.deeplearning.ai/the-batch/"
+
+# Configure SSL once at the module level
+if hasattr(ssl, '_create_unverified_context'):
+    ssl._create_default_https_context = ssl._create_unverified_context
+
+def extract_date(date_str):
+    """Extract date from various formats using regex patterns"""
+    try:
+        # Try different patterns to match various date formats
+        
+        # Pattern 1: Standard RFC format like "Mon, 14 Apr 2025 10:00:00 GMT"
+        pattern1 = r'(?:\w+,\s+)?(\d{1,2}\s+\w{3}\s+\d{4})'
+        match = re.search(pattern1, date_str)
+        if match:
+            date_str = match.group(1)
+            return pd.to_datetime(date_str, format='%d %b %Y')
+        
+        # Pattern 2: Simple format like "14 Apr 2025"
+        pattern2 = r'(\d{1,2}\s+\w{3}\s+\d{4})'
+        match = re.search(pattern2, date_str)
+        if match:
+            return pd.to_datetime(match.group(1), format='%d %b %Y')
+        
+        # Pattern 3: ISO format like "2025-04-14"
+        pattern3 = r'(\d{4}-\d{2}-\d{2})'
+        match = re.search(pattern3, date_str)
+        if match:
+            return pd.to_datetime(match.group(1))
+        
+        # Pattern 4: Format like "Mar 12, 2025"
+        pattern4 = r'(\w{3}\s+\d{1,2},\s+\d{4})'
+        match = re.search(pattern4, date_str)
+        if match:
+            return pd.to_datetime(match.group(1), format='%b %d, %Y')
+        
+        # If none of the patterns match, return original parsed date
+        return pd.to_datetime(date_str)
+    except:
+        # If all else fails, return NaT
+        return pd.NaT
+
+def clean_html(text):
+    """Clean HTML tags from text"""
+    try:
+        soup = BeautifulSoup(text, "html.parser")
+        return soup.get_text()
+    except Exception as e:
+        print(f"Error cleaning HTML: {e}")
+        return text
+
+def extract_image_url(entry, description):
+    """Extract image URL from RSS entry if available"""
+    try:
+        # Check for media:content
+        if hasattr(entry, 'media_content') and entry.media_content:
+            for media in entry.media_content:
+                if isinstance(media, dict) and 'url' in media:
+                    return media['url']
+        
+        # Check for media:thumbnail
+        if hasattr(entry, 'media_thumbnail') and entry.media_thumbnail:
+            for media in entry.media_thumbnail:
+                if isinstance(media, dict) and 'url' in media:
+                    return media['url']
+        
+        # Check for enclosures
+        if hasattr(entry, 'enclosures') and entry.enclosures:
+            for enclosure in entry.enclosures:
+                if isinstance(enclosure, dict) and 'url' in enclosure and enclosure.get('type', '').startswith('image/'):
+                    return enclosure['url']
+        
+        # Try to extract from description using BeautifulSoup
+        if description:
+            soup = BeautifulSoup(description, "html.parser")
+            
+            # First, check meta tags for twitter:image
+            meta_img = soup.find('meta', attrs={'name': 'twitter:image'})
+            if meta_img and meta_img.has_attr('content'):
+                return meta_img['content']
+            
+            # Then check for regular img tags
+            img_tag = soup.find('img')
+            if img_tag and img_tag.has_attr('src'):
+                return img_tag['src']
+            
+            # Try to extract image URL from HTML
+            img_match = re.search(r'<img[^>]+src=[\'"]([^\'"]+)[\'"]', description)
+            if img_match:
+                return img_match.group(1)
+        
+        # No image found
+        return None
+    except Exception as e:
+        print(f"Error extracting image URL: {e}")
+        return None
+
+def fetch_single_feed(link_source_tuple):
+    """Fetch a single RSS feed and return its entries"""
+    link, source = link_source_tuple
+    entries = {"Title": [], "Link": [], "Published": [], "Description": [], "Source": [], "Image": []}
+    
+    try:
+        feed = feedparser.parse(link)
+        
+        for entry in feed.entries:
+            title = entry.get("title", "No Title")
+            link = entry.get("link", "No Link")
+            published = entry.get("published", "No Date")
+            description = entry.get("description", "No Description")
+            
+            # Extract image URL
+            image_url = extract_image_url(entry, description)
+            
+            entries["Title"].append(title)
+            entries["Link"].append(link)
+            entries["Published"].append(published)
+            entries["Description"].append(description)
+            entries["Source"].append(source)
+            entries["Image"].append(image_url)  # Add image URL
+            
+    except Exception as e:
+        print(f"Error fetching {link}: {e}")
+    
+    return entries
+
+def fetch_feed(links):
+    """Fetch multiple RSS feeds in parallel"""
+    all_entries = {"Title": [], "Link": [], "Published": [], "Description": [], "Source": [], "Image": []}
+    
+    # Use ThreadPoolExecutor to fetch feeds in parallel
+    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
+        future_to_link = {executor.submit(fetch_single_feed, (link, source)): (link, source) 
+                         for link, source in links.items()}
+        
+        for future in concurrent.futures.as_completed(future_to_link):
+            link, source = future_to_link[future]
+            try:
+                result = future.result()
+                # Merge results into all_entries
+                for key in all_entries:
+                    all_entries[key].extend(result[key])
+            except Exception as e:
+                print(f"Exception for {link}: {e}")
+    
+    # Create a DataFrame from all entries
+    df = pd.DataFrame(all_entries)
+    return df
+
+def scrape_the_batch_articles():
+    all_entries = {"Title": [], "Link": [], "Published": [], "Description": [], "Source": [], "Image": []}
+    
+    try:
+        res = requests.get(URL)
+        soup = BeautifulSoup(res.text, "html.parser")
+
+        articles = soup.find_all("article")
+
+        for article in articles:
+            # Link
+            link_tag = article.find("a", href=True)
+            link = "https://www.deeplearning.ai" + link_tag["href"] if link_tag else "#"
+
+            # Title
+            title_tag = article.find("h2")
+            title = title_tag.get_text(strip=True) if title_tag else "No title"
+
+            # Summary
+            summary_tag = article.find("div", class_="text-sm")
+            summary = summary_tag.get_text(strip=True) if summary_tag else ""
+
+            # Date (based on div with specific class)
+            date_tag = article.find("div", class_="text-slate-500")
+            date_str = date_tag.get_text(strip=True) if date_tag else ""
+            
+            # Image
+            img_tag = article.find("img")
+            image_url = img_tag["src"] if img_tag and img_tag.has_attr("src") else None
+
+            try:
+                parsed_date = datetime.strptime(date_str, "%b %d, %Y")
+            except Exception as e:
+                parsed_date = None
+
+            if parsed_date:
+                all_entries["Title"].append(title)
+                all_entries["Description"].append(summary)
+                all_entries["Link"].append(link)
+                all_entries["Published"].append(date_str)
+                all_entries["Source"].append("deeplearning.ai")
+                all_entries["Image"].append(image_url)
+        
+        return pd.DataFrame(all_entries)
+    except Exception as e:
+        print(f"Error scraping The Batch: {e}")
+        return pd.DataFrame()
+
+def extract_and_clean_data(df):
+    """Process and clean the feed data"""
+    if df.empty:
+        return df
+        
+    try:
+        # Apply the custom date extraction function
+        df['date'] = df['Published'].apply(extract_date)
+        
+        # Drop rows with invalid dates
+        df = df.dropna(subset=['date'])
+        
+        # Drop the original 'Published' column
+        df.drop(columns=['Published'], inplace=True)
+        
+        # Filter for the last 30 days (increased from 7 for more content)
+        today = datetime.now()
+        thirty_days_ago = today - timedelta(days=30)
+        df_filtered = df[(df['date'] >= thirty_days_ago) & (df['date'] <= today)]
+        
+        # Sort by date in descending order
+        df_filtered = df_filtered.sort_values(by='date', ascending=False)
+        
+        # Clean HTML and limit description length in one step
+        df_filtered['Description'] = df_filtered['Description'].apply(
+            lambda x: clean_html(x)[:500].replace("\n", "")
+        )
+        
+        return df_filtered
+        
+    except Exception as e:
+        print(f"An error occurred while processing the data: {e}")
+        return pd.DataFrame()
+
+def main():
+    # RSS links
+    links = {
+        "https://bair.berkeley.edu/blog/feed.xml": "The Berkeley Artificial Intelligence Research Blog",
+        "https://feeds.feedburner.com/nvidiablog": "NVDIA Blog",
+        "https://www.microsoft.com/en-us/research/feed/": "Microsoft Research",
+        "https://www.sciencedaily.com/rss/computers_math/artificial_intelligence.xml": "Science Daily",
+        "https://research.facebook.com/feed/": "META Research",
+        "https://openai.com/news/rss.xml": "OpenAI News",
+        "https://deepmind.google/blog/feed/basic/": "Google DeepMind Blog",
+        "https://news.mit.edu/rss/topic/artificial-intelligence2": "MIT News - Artificial intelligence",
+        "https://www.technologyreview.com/topic/artificial-intelligence/feed": "MIT Technology Review - Artificial intelligence",
+        "https://www.wired.com/feed/tag/ai/latest/rss": "Wired: Artificial Intelligence Latest",
+        "https://raw.githubusercontent.com/Olshansk/rss-feeds/refs/heads/main/feeds/feed_ollama.xml": "Ollama Blog",
+        "https://newsroom.ibm.com/press-releases-artificial-intelligence?pagetemplate=rss": "IBM - Announcements (Artificial intelligence)"
+    }
+
+    # Fetch data from The Batch
+    batch_df = scrape_the_batch_articles()
+    
+    # Fetch data from RSS feeds
+    rss_df = fetch_feed(links)
+    
+    # Combine both dataframes
+    combined_df = pd.concat([batch_df, rss_df], ignore_index=True)
+    
+    # Process and clean data
+    final_df = extract_and_clean_data(combined_df)
+    
+    return final_df
+
+if __name__ == "__main__":
+    df = main()
+    print(df.head())
+    df.to_excel("ai_news.xlsx")

main_page.py

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+import streamlit as st
+import pandas as pd
+from datetime import datetime, timedelta
+from fetch_data import main
+from PIL import Image
+import base64
+import os
+
+# Yerel placeholder görüntüyü yüklemek için fonksiyon
+def get_base64_encoded_image(image_path):
+    with open(image_path, "rb") as img_file:
+        return base64.b64encode(img_file.read()).decode('utf-8')
+
+# Placeholder görüntü yolu - kendi dosya yolunuzu buraya yazın
+PLACEHOLDER_IMAGE_PATH = "placeholder.jpeg"  # Bu dosyanın Python kodunuzla aynı dizinde olduğunu varsayıyorum
+
+# Görüntüyü base64 formatına dönüştür (eğer dosya mevcutsa)
+if os.path.exists(PLACEHOLDER_IMAGE_PATH):
+    img_base64 = get_base64_encoded_image(PLACEHOLDER_IMAGE_PATH)
+    PLACEHOLDER_IMAGE = f"data:image/jpeg;base64,{img_base64}"
+else:
+    # Dosya bulunamazsa yedek olarak online bir görsel kullan
+    PLACEHOLDER_IMAGE = "https://img.freepik.com/free-vector/artificial-intelligence-ai-robot-server-room-digital-technology-banner_39422-794.jpg"
+    st.warning(f"Placeholder image not found at {PLACEHOLDER_IMAGE_PATH}. Using fallback image.")
+
+all_sources = ["The Berkeley Artificial Intelligence Research Blog","NVDIA Blog","Microsoft Research","Science Daily","META Research","OpenAI News",
+               "Google DeepMind Blog","MIT News - Artificial intelligence","MIT Technology Review - Artificial intelligence","Wired: Artificial Intelligence Latest",
+               "Ollama Blog","IBM - Announcements (Artificial intelligence)","deeplearning.ai"]
+
+
+# Use Streamlit's built-in caching
+@st.cache_data(ttl=60)  # Cache for 1 minute
+def get_data():
+    with st.spinner('Fetching latest AI news...'):
+        return main()
+
+def run_dashboard():
+    st.title("Latest AI News")
+    
+    # Add sidebar for all filters
+    with st.sidebar:
+        st.header("Filter Options")
+        
+        # Add a refresh button at the top of sidebar
+        if st.button("Refresh News"):
+            # Clear the cache and get fresh data
+            st.cache_data.clear()
+            st.rerun()
+    
+    # Load data with caching
+    try:
+        df = get_data()
+        
+        # Check if df is empty
+        if df.empty:
+            st.error("No news data available. Please try refreshing later.")
+            return
+            
+        # Get min and max dates
+        min_date = df['date'].min()
+        max_date = df['date'].max()
+        
+        # Calculate default date range (last 7 days)
+        default_end_date = max_date
+        default_start_date = default_end_date - timedelta(days=7)
+        if default_start_date < min_date:
+            default_start_date = min_date
+        
+        # Continue with sidebar for filters
+        with st.sidebar:
+            # Date filter
+            selected_dates = st.date_input(
+                "Choose dates",
+                value=(default_start_date, default_end_date),
+                min_value=min_date,
+                max_value=max_date
+            )
+            
+            # Handle single date selection
+            if len(selected_dates) == 1:
+                start_date = selected_dates[0]
+                end_date = selected_dates[0]
+            else:
+                start_date, end_date = selected_dates
+            
+            
+            # Get unique sources from predefined list
+            sources = sorted(all_sources)
+            
+            # Use multiselect without "All" option
+            selected_sources = st.multiselect(
+                "Choose sources",
+                options=sources
+            )
+            
+        
+        # Main content area for displaying news
+        # Convert dates to datetime
+        start_date = pd.to_datetime(start_date)
+        end_date = pd.to_datetime(end_date)
+        
+        # Filter by date range
+        df_filtered = df[(df['date'] >= start_date) & (df['date'] <= end_date)]
+        
+        # Handle source filtering
+        if selected_sources:  # If sources are selected
+            # Filter by selected sources
+            df_filtered = df_filtered[df_filtered['Source'].isin(selected_sources)]
+        # If no sources selected, show all (no additional filtering needed)
+        
+        # Display results
+        if len(df_filtered) > 0:
+            
+            # Apply CSS styling for cards
+            st.markdown("""
+            <style>
+            .news-card {
+                border-radius: 20px;
+                padding: 0;
+                margin-bottom: 20px;
+                background-color: #E8E8E8;
+                height: 480px;
+                overflow: hidden;
+                position: relative;
+                box-shadow: 0 3px 6px rgba(0, 0, 0, 0.1);
+            }
+            .news-image-container {
+                width: 100%;
+                height: 220px;
+                overflow: hidden;
+                display: flex;
+                align-items: center;
+                justify-content: center;
+                padding: 10px 10px 0 10px;
+            }
+            .news-image {
+                width: 100%;
+                height: 100%;
+                object-fit: cover;
+                border-radius: 12px;
+            }
+            .news-content {
+                padding: 12px 15px;
+            }
+            .news-title {
+                font-weight: bold;
+                font-size: 16px;
+                margin-bottom: 10px;
+                color: #000;
+                line-height: 1.3;
+                max-height: 105px;
+                display: -webkit-box;
+                -webkit-line-clamp: 4;
+                -webkit-box-orient: vertical;
+                overflow: hidden;
+            }
+            .news-meta {
+                display: flex;
+                justify-content: space-between;
+                margin-bottom: 12px;
+                align-items: center;
+                border-bottom: 1px solid #ddd;
+                padding-bottom: 8px;
+            }
+            .news-source {
+                color: #555;
+                font-size: 12px;
+                font-style: italic;
+            }
+            .news-date {
+                color: #555;
+                font-size: 12px;
+                text-align: right;
+                font-style: italic;
+            }
+            .news-description {
+                color: #333;
+                font-size: 13px;
+                padding-bottom: 10px;
+                line-height: 1.4;
+                display: -webkit-box;
+                -webkit-line-clamp: 5;
+                -webkit-box-orient: vertical;
+                overflow: hidden;
+                height: 90px;
+            }
+            </style>
+            """, unsafe_allow_html=True)
+            
+            # Create 3-column layout
+            num_cols = 3
+            
+            # Process news items in groups of 3 for the grid
+            for i in range(0, len(df_filtered), num_cols):
+                cols = st.columns(num_cols)
+                
+                # Get the current batch of news items
+                current_batch = df_filtered.iloc[i:i+num_cols]
+                
+                # Display each news item in its column
+                for j, (_, row) in enumerate(current_batch.iterrows()):
+                    if j < len(cols):  # Ensure we have a column for this item
+                        with cols[j]:
+                            # Eğer kaynak deeplearning.ai ise veya geçerli bir görüntü yoksa, placeholder kullan
+                            if row['Source'] == "deeplearning.ai" or not pd.notna(row.get('Image')) or row.get('Image') is None:
+                                image_url = PLACEHOLDER_IMAGE
+                            else:
+                                image_url = row['Image']
+                            
+                            # Format the date
+                            date_str = row['date'].strftime('%d %b %Y')
+                            
+                            # Truncate description if it's too long
+                            description = row['Description'][:150] + "..." if len(row['Description']) > 150 else row['Description']
+                            
+                            # Display card with HTML
+                            html_content = f"""
+                            <a href="{row['Link']}" target="_blank" style="text-decoration: none; color: inherit;">
+                                <div class="news-card">
+                                    <div class="news-image-container">
+                                        <img src="{image_url}" class="news-image" onerror="this.onerror=null;this.src='{PLACEHOLDER_IMAGE}';">
+                                    </div>
+                                    <div class="news-content">
+                                        <div class="news-title">{row['Title']}</div>
+                                        <div class="news-meta">
+                                            <div class="news-source">{row['Source']}</div>
+                                            <div class="news-date">{date_str}</div>
+                                        </div>
+                                        <div class="news-description">{description}</div>
+                                    </div>
+                                </div>
+                            </a>
+                            """
+                            st.markdown(html_content, unsafe_allow_html=True)
+        else:
+            st.warning("No news found with the selected filters. Please adjust your date range or source selection.")
+    
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+        st.info("Try refreshing the data using the button above.")
+
+if __name__ == '__main__':
+    run_dashboard()

requirements.txt

@@ -0,0 +1,72 @@
+altair==5.5.0
+appnope==0.1.4
+asttokens==3.0.0
+attrs==25.3.0
+beautifulsoup4==4.13.3
+blinker==1.9.0
+bs4==0.0.2
+cachetools==5.5.2
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+comm==0.2.2
+debugpy==1.8.14
+decorator==5.2.1
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