import streamlit as st
from transformers import pipeline
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
import os

# Load Hugging Face Token (Ensure it's set in Env Variables)
HF_TOKEN = os.getenv("HF_TOKEN")

# ✅ Optimized Model (Flan-T5 for Lower Memory Usage)
MODEL_NAME = "google/flan-t5-large"

# 📌 Load Model Efficiently (Avoid Reloading)
@st.cache_resource
def load_model():
    try:
        return pipeline("text2text-generation", model=MODEL_NAME, token=HF_TOKEN)
    except Exception as e:
        st.error(f"❌ Error loading model: {str(e)}")
        return None

# Load once and reuse
generator = load_model()

# 📌 Function to Generate Functional Requirement Document
def generate_functional_requirements(topic):
    if generator is None:
        return "Error: Model failed to load."

    sections = {
        "Introduction": [
            "Overview and Purpose",
            "Intended Users"
        ],
        "Scope": [
            "System Description",
            "Key Functionalities"
        ],
        "Functional Specifications": [
            "User Roles",
            "Core Features"
        ],
        "Security & Compliance": [
            "Regulatory Requirements",
            "Data Protection"
        ],
        "Future Enhancements": [
            "Potential Feature Expansions",
            "Roadmap & Next Steps"
        ]
    }

    document = []  # Store paragraphs in a structured way
    styles = getSampleStyleSheet()

    for section, subsections in sections.items():
        document.append(Paragraph(f"<b>{section}</b>", styles['Title']))
        document.append(Spacer(1, 10))

        for subsection in subsections:
            prompt = f"Write a **detailed 300-word section** on '{subsection}' for the topic '{topic}' in banking. Provide structured paragraphs with examples."

            output = generator(prompt, max_length=1024, do_sample=True, temperature=0.7)

            if output and isinstance(output, list) and len(output) > 0 and "generated_text" in output[0]:
                document.append(Paragraph(f"<b>{subsection}</b>", styles['Heading2']))
                document.append(Spacer(1, 6))
                document.append(Paragraph(output[0]["generated_text"], styles['Normal']))
                document.append(Spacer(1, 10))
            else:
                return "Error: Model failed to generate text."

        document.append(PageBreak())  # Add a page break after each major section

    return document

# 📌 Function to Save Generated Content as PDF
def save_to_pdf(content, filename):
    if not content:
        st.error("❌ Error: No content available to write to the PDF.")
        return

    doc = SimpleDocTemplate(filename, pagesize=letter)
    doc.build(content)

# 📌 Streamlit UI
def main():
    st.title("📄 AI-Powered Functional Requirement Generator for Banking")
    
    banking_topics = [
        "Core Banking System", "Loan Management System", "Payment Processing Gateway",
        "Risk and Fraud Detection", "Regulatory Compliance Management", "Digital Banking APIs",
        "Customer Onboarding & KYC", "Treasury Management", "Wealth & Portfolio Management"
    ]
    
    topic = st.selectbox("Select a Banking Functional Requirement Topic", banking_topics)
    
    if st.button("Generate Functional Requirement Document"):
        with st.spinner("Generating... This may take a while."):
            content = generate_functional_requirements(topic)

            if isinstance(content, str) and "Error" in content:
                st.error(content)
            else:
                filename = "functional_requirement.pdf"
                save_to_pdf(content, filename)
                
                st.success("✅ Document Generated Successfully!")
                st.download_button("📥 Download PDF", data=open(filename, "rb"), file_name=filename, mime="application/pdf")
                os.remove(filename)  # Cleanup after download

if __name__ == "__main__":
    main()