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()