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("""
""", 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"""
{row['Title']}
{description}
"""
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()