app.py

import streamlit as st
from gnews import GNews
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import pipeline
from gnews import GNews
from newspaper import Article
import pandas as pd 
import numpy as np
import requests
import json
import os
import streamlit as st
import pydeck as pdk
import pandas as pd
import requests
import os
import json

# Make sure to define or import other necessary functions or variables here

def page_va



def fetch_news(topic):
    google_news = GNews(language='german', country='Germany')  # You can customize this
    news_list = google_news.get_news(topic)
    
    articles = []
    for news in news_list[:5]:  # Get top 5 news articles
        articles.append({
            'title': news['title'],
            'published_date': news['published date'],
            'description': news['description'],
            'url': news['url'],
            'publisher': news['publisher']
        })
    return articles






def page_trending_niche():
    st.title("What is trending in my niche?")
    niche = st.text_input('Enter your niche', 'Technology')
    # page_trending_niche function
    if niche:
        news_items = fetch_news(niche)
        article_titles = [item['title'] for item in news_items]
        selected_article = st.selectbox("Select an article to generate a social media post about:", article_titles)
        selected_article_description = next((item['description'] for item in news_items if item['title'] == selected_article), None)
        
        # Save the selected article's description in the session state to use in another page
        st.session_state['selected_article_description'] = selected_article_description

        for item in news_items:
            st.write(f"**Title:** {item['title']}")
            st.write(f"**Published Date:** {item['published_date']}")
            st.write(f"**Description:** {item['description']}")
            st.write(f"**Publisher:** {item['publisher']}")
            st.write(f"**URL:** [Read more]({item['url']})")
            st.write("---")


def fetch_full_article(url):
    """Fetches the full text of an article given its URL."""
    article = Article(url)
    article.download()
    article.parse()
    return article.text
    


# Initialize the summarization pipeline with BART
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def split_text_into_chunks(text, chunk_size, overlap_size):
    chunks = []
    index = 0
    while index < len(text):
        # End index for the current chunk
        end_index = index + chunk_size
        # Extend the end index to include the overlap, if possible
        end_index_with_overlap = min(end_index + overlap_size, len(text))
        # Extract the chunk with the overlap
        chunk = text[index:end_index_with_overlap]
        chunks.append(chunk)
        # Move the index to the start of the next chunk, which is end_index
        index = end_index
    return chunks
    

def generate_social_media_post(article_text):
    chunk_size = 900  # This is close to the model's maximum length for BART
    overlap_size = 50  # Overlap size to ensure continuity in the text
    chunks = split_text_into_chunks(article_text, chunk_size, overlap_size)
    
    summarized_text = ''
    for chunk in chunks:
        # Call the summarizer for each chunk
        summary = summarizer(chunk, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
        summarized_text += summary + ' '
    return summarized_text.strip()



        
def page_article_to_social_post():
    st.title("Article to Social Media Post")
    
    # User input for niche
    niche = st.text_input('Enter your niche', 'Technology')
    
    if niche:
        # Fetch news articles
        google_news = GNews(language='german', country='Germany')  # You can customize this
        news_list = google_news.get_news(niche)
        
        if not news_list:
            st.write("No news found for the given niche.")
            return
        
        # Display article titles in a selectbox
        article_titles = [news['title'] for news in news_list[:5]]
        selected_title = st.selectbox("Select an article:", article_titles)
        
        selected_article = next((item for item in news_list if item['title'] == selected_title), None)
        if selected_article:
            selected_url = selected_article['url']


            if st.button('Fetch Full Article'):
                # Fetch the full article text
                article_text = fetch_full_article(selected_url)
                
                # Use an expander to display the article text in a scrollable view
                with st.expander("See full article"):
                    st.text_area("Article Text", article_text, height=500)  # Height is in pixels

                
                # Store the full article text in session state for later use
                st.session_state.full_article_text = article_text

            if st.button('Generate Social Media Post') and 'full_article_text' in st.session_state:
                with st.spinner('Generating...'):
                    # Generate a summary based on the full article text
                    post_content = generate_social_media_post(st.session_state.full_article_text)
                    st.success('Generated Content:')
                    st.write(post_content)


import os
import requests
import streamlit as st
import pandas as pd

def get_route(start_coord, end_coord):
    api_key = os.getenv('OPENSTREET_API')
    if not api_key:
        st.error("API key not found. Please set the ORS_API_KEY environment variable.")
        return
    
    headers = {
        'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',
    }
    url = f"https://api.openrouteservice.org/v2/directions/driving-car?api_key={api_key}&start={start_coord[0]},{start_coord[1]}&end={end_coord[0]},{end_coord[1]}"
    
    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        route = response.json()['features'][0]['geometry']['coordinates']
        return route
    else:
        st.error(f"Failed to get route data. Status code: {response.status_code}")
        return None

def page_vacation():
    st.title("Route Finder with OpenRouteService")

    # Inputs for start and end coordinates
    st.header("Start Point")
    start_lat = st.number_input("Start Latitude", value=49.41461)
    start_lon = st.number_input("Start Longitude", value=8.681495)

    st.header("End Point")
    end_lat = st.number_input("End Latitude", value=49.420318)
    end_lon = st.number_input("End Longitude", value=8.687872)

    if st.button("Find Route"):
        route = get_route([start_lon, start_lat], [end_lon, end_lat])
        if route:
            # Convert the route to a DataFrame
            route_df = pd.DataFrame(route, columns=['lon', 'lat'])
            
            # Display the route using PyDeck
            st.pydeck_chart(pdk.Deck(
                map_style='mapbox://styles/mapbox/light-v9',
                initial_view_state=pdk.ViewState(
                    latitude=49.41461,
                    longitude=8.681495,
                    zoom=11,
                    pitch=50,
                ),
                layers=[
                    pdk.Layer(
                        'LineLayer',
                        data=route_df,
                        get_source_position='[lon, lat]',
                        get_target_position='[lon, lat]',
                        get_color='[255, 0, 0, 160]',
                        get_width=5,
                        pickable=True
                    ),
                ],
            ))

# Setup the sidebar with page selection
st.sidebar.title("Anne's Current Projects :star2:")

page = st.sidebar.selectbox(
    'What project do you like to see first?',
    ('trending_niche', 'page_article_to_social_post', 'Vacation Page'))

# Display the selected page
if page == 'trending_niche':
    page_trending_niche()
elif page == 'page_article_to_social_post':
    page_article_to_social_post()
elif page == 'Vacation Page':
    page_vacation()