app.py

import streamlit as st
from transformers import pipeline

# Initialize a text generation pipeline
generator = pipeline('text-generation', model='dbmdz/german-gpt2')

# Define the pages
def page_trending_niche():
    
    # Using st.columns to create a two-column layout
    col1, col2 = st.columns([3, 1])
    with col1:
        st.title("What is trending in my niche?")
    with col2:
        st.image('Robot.png', use_column_width=True)


# Define the pages
def page_social_media_generator():
    
    # Using st.columns to create a two-column layout
    col1, col2 = st.columns([3, 1])
    with col1:
        st.title("German Medical Content Manager")
    with col2:
        st.image('Content_Creation_Pic.png', use_column_width=True)

    
    input_topic = st.text_input('Enter a medical topic', 'Type 1 Diabetes')
    st.write(f"Creating social media content for: {input_topic}")

    def generate_content(topic):
        generated_text = generator(f"Letzte Nachrichten über {topic}:", max_length=50, num_return_sequences=1)
        return generated_text[0]['generated_text']

    if st.button('Generate Social Media Post'):
        with st.spinner('Generating...'):
            post_content = generate_content(input_topic)
            st.success('Generated Content:')
            st.write(post_content)

    st.write('Generated social media posts will appear here after clicking the "Generate" button.')

def page_test():
    st.title('Test Page')
    st.write('This is a test page with a test name.')

# 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', 'Social Media Content Generator', 'Test Page'))


# Display the selected page
if page == 'trending_niche':
    page_trending_niche()
elif page == 'Social Media Content Generator':
    page_social_media_generator()
elif page == 'Test Page':
    page_test()