Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from reportlab.lib import colors
+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 Background",
+            "Purpose of the System",
+            "Intended Users and Audience"
+        ],
+        "Scope": [
+            "General System Description",
+            "Key Functionalities & Modules",
+            "Out of Scope Features"
+        ],
+        "Functional Specifications": [
+            "User Roles & Access Levels",
+            "Main Features and Use Cases",
+            "Transaction Handling & Workflows"
+        ],
+        "Security & Compliance": [
+            "Regulatory Requirements",
+            "Data Protection & Encryption",
+            "Fraud Detection Mechanisms"
+        ],
+        "User Interface": [
+            "Wireframes and UI/UX Considerations",
+            "Navigation & Accessibility Features"
+        ],
+        "System Architecture": [
+            "High-Level Architecture",
+            "Technology Stack Used",
+            "API Integrations"
+        ],
+        "Performance & Scalability": [
+            "Expected Load & System Throughput",
+            "Response Time Expectations",
+            "Scalability Strategies"
+        ],
+        "Data Management": [
+            "Data Flow and Storage",
+            "Backup & Disaster Recovery Strategies"
+        ],
+        "Future Enhancements": [
+            "Potential Feature Expansions",
+            "Long-Term Roadmap",
+            "Next Steps in Development"
+        ]
+    }
+
+    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 500-word section** on '{subsection}' for the topic '{topic}' in banking. Provide structured paragraphs with examples."
+
+            for _ in range(2):  # Increase iterations per subsection
+                output = generator(prompt, max_length=2048, 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()