app.py

import streamlit as st
import requests
from fpdf import FPDF
import os
import time
from datetime import datetime
import groq

# Groq API key (replace with your key or use environment variable)
api_key = os.getenv("GROQ_API_KEY", "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")

# Initialize Groq client
groq_client = groq.Client(api_key=api_key)

# Function to call Groq API with rate limit handling
def call_groq_api(prompt):
    try:
        response = groq_client.chat.completions.create(
            model="llama-3.3-70b-versatile",  # Correct model name
            messages=[
                {"role": "user", "content": prompt}
            ]
        )
        return response.choices[0].message.content
    except Exception as err:
        st.error(f"Error: {err}")
        return f"Error: {err}"

# Function to analyze a single requirement
def analyze_requirement(requirement):
    # Detect requirement type
    type_prompt = f"Classify the following requirement as Functional or Non-Functional:\n\n{requirement}\n\nType:"
    req_type = call_groq_api(type_prompt)

    # Identify stakeholders
    stakeholders_prompt = f"Identify the stakeholders for the following requirement:\n\n{requirement}\n\nStakeholders:"
    stakeholders = call_groq_api(stakeholders_prompt)

    # Classify domain
    domain_prompt = f"Classify the domain for the following requirement (e.g., Bank, Healthcare, etc.):\n\n{requirement}\n\nDomain:"
    domain = call_groq_api(domain_prompt)

    # Detect defects
    defects_prompt = f"""Analyze the following requirement and identify ONLY MAJOR defects (e.g., Ambiguity, Incompleteness, etc.). 
    If the requirement is clear and complete, respond with 'No defects.' 
    Requirement: {requirement}
    Defects:"""
    defects = call_groq_api(defects_prompt)

    # Rewrite requirement
    rewritten_prompt = f"""Rewrite the following requirement in 1-2 sentences to address the defects:\n\n{requirement}\n\nRewritten:"""
    rewritten = call_groq_api(rewritten_prompt).strip()

    return {
        "Requirement": requirement,
        "Type": req_type,
        "Stakeholders": stakeholders,
        "Domain": domain,
        "Defects": defects,
        "Rewritten": rewritten
    }

# Function to rewrite requirement concisely
def rewrite_requirement(requirement, defects):
    if "no defects" in defects.lower():
        return "No modification needed."

    # If defects are found, generate a concise and clear rewritten requirement
    prompt = f"""Rewrite the following requirement to address the defects listed below. Ensure the rewritten requirement is clear, concise, and free of defects. It should be no more than 1-2 sentences.
    
    Original Requirement: {requirement}
    
    Defects: {defects}
    
    Rewritten Requirement:"""
    response = call_groq_api(prompt)
    return response.strip()

# Function to generate a PDF report with professional formatting
def generate_pdf_report(results):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)

    # Add watermark
    pdf.set_font("Arial", 'B', 50)
    pdf.set_text_color(230, 230, 230)  # Light gray color for watermark
    pdf.rotate(45)  # Rotate the text for watermark effect
    pdf.text(60, 150, "AI Powered Requirement Analysis")
    pdf.rotate(0)  # Reset rotation

    # Add title and date/time
    pdf.set_font("Arial", 'B', 16)
    pdf.set_text_color(0, 0, 0)  # Black color for title
    pdf.cell(200, 10, txt="AI Powered Requirement Analysis and Defect Detection using LLaMA Model", ln=True, align='C')
    pdf.set_font("Arial", size=12)
    pdf.cell(200, 10, txt=f"Report Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True, align='C')
    pdf.ln(10)  # Add some space

    # Add requirements analysis
    pdf.set_font("Arial", size=12)
    for i, result in enumerate(results, start=1):
        # Check if we need a new page
        if pdf.get_y() > 250:  # If the content is near the bottom of the page
            pdf.add_page()  # Add a new page
            pdf.set_font("Arial", 'B', 16)
            pdf.cell(200, 10, txt="AI Powered Requirement Analysis and Defect Detection using LLaMA Model", ln=True, align='C')
            pdf.set_font("Arial", size=12)
            pdf.cell(200, 10, txt=f"Report Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True, align='C')
            pdf.ln(10)  # Add some space

        # Add requirement details
        pdf.set_font("Arial", 'B', 14)
        pdf.multi_cell(200, 10, txt=f"Requirement R{i}: {result['Requirement']}", align='L')
        pdf.set_font("Arial", size=12)
        pdf.multi_cell(200, 10, txt=f"Type: {result['Type']}", align='L')
        pdf.multi_cell(200, 10, txt=f"Stakeholders: {result['Stakeholders']}", align='L')
        pdf.multi_cell(200, 10, txt=f"Domain: {result['Domain']}", align='L')
        pdf.multi_cell(200, 10, txt=f"Defects: {result['Defects']}", align='L')
        pdf.multi_cell(200, 10, txt=f"Rewritten: {result['Rewritten']}", align='L')
        pdf.multi_cell(200, 10, txt="-" * 50, align='L')
        pdf.ln(5)  # Add some space between requirements

    pdf_output = "requirements_report.pdf"
    pdf.output(pdf_output)
    return pdf_output

# Streamlit app
def main():
    st.title("AI Powered Requirement Analysis and Defect Detection using Large Language Model LLaMA")
    st.markdown("**Team:** MSSE-31")
    st.markdown("**Model:** LLaMA-3.3-70b-Versatile")

    # Input requirements manually
    input_text = st.text_area("Enter your requirements (one per line or separated by periods):")
    requirements = []
    if input_text:
        # Split by periods or newlines
        requirements = [req.strip() for req in input_text.replace("\n", ".").split(".") if req.strip()]

    # Analyze requirements
    if st.button("Analyze Requirements"):
        if not requirements:
            st.warning("Please enter requirements.")
        else:
            results = []
            for req in requirements:
                if req.strip():  # Ignore empty lines
                    results.append(analyze_requirement(req.strip()))

            # Display results
            st.subheader("Analysis Results")
            for i, result in enumerate(results, start=1):
                st.write(f"### Requirement R{i}: {result['Requirement']}")
                st.write(f"**Type:** {result['Type']}")
                st.write(f"**Stakeholders:** {result['Stakeholders']}")
                st.write(f"**Domain:** {result['Domain']}")
                st.write(f"**Defects:** {result['Defects']}")
                st.write(f"**Rewritten:** {result['Rewritten']}")
                st.write("---")

            # Generate and download PDF report
            pdf_report = generate_pdf_report(results)
            with open(pdf_report, "rb") as f:
                st.download_button(
                    label="Download PDF Report",
                    data=f,
                    file_name="requirements_report.pdf",
                    mime="application/pdf"
                )

# Run the app
if __name__ == "__main__":
    main()