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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 | |
| # 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() |