Spaces:
Running
Running
File size: 9,932 Bytes
85468c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
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() |