import streamlit as st
from transformers import pipeline

# Set the page configuration
st.set_page_config(
    page_title="News Report Generator",
    layout="centered",
    initial_sidebar_state="auto",
)

# Load the model with caching to improve performance
@st.cache_resource
def load_model():
    model_name = "gpt2"  # Change to a larger model for more advanced outputs if needed
    return pipeline("text-generation", model=model_name)

text_generator = load_model()

# Streamlit app UI
st.title("📰 News Report Generator")
st.markdown("Generate a compelling news report based on any given scenario.")

# Input for the scenario and word count
scenario = st.text_area(
    "📜 Enter the Scenario:",
    placeholder="Describe the scenario for the news report...",
    height=150,
)
word_count = st.number_input(
    "🔢 Desired Word Count:",
    min_value=50,
    max_value=1000,
    step=50,
    help="Specify the approximate number of words for the generated report.",
)

# Generate News Report button
if st.button("📝 Generate News Report"):
    if not scenario.strip():
        st.error("❌ Please provide a scenario to generate the news report.")
    else:
        with st.spinner("Generating your news report..."):
            try:
                # Generate the news report
                prompt = f"Write a news report on the following scenario: {scenario}\n\n"
                generated_text = text_generator(prompt, max_length=int(word_count), num_return_sequences=1)
                
                # Display the result
                st.subheader("📰 Generated News Report")
                st.text_area("Report", value=generated_text[0]["generated_text"], height=400)
            except Exception as e:
                st.error(f"❌ An error occurred: {e}")