app.py

import streamlit as st
from transformers import pipeline

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

st.title('German Medical Content Manager')

# Sidebar for user input
st.sidebar.header('User Input Options')
input_topic = st.sidebar.text_input('Enter a medical topic', 'Type 1 Diabetes')

# Main Page
st.write(f"Creating social media content for: {input_topic}")

# Function to generate text based on the 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.')