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

# Groq API key (replace with your actual key)
groq_api_key = "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"

# Function to call Groq Llama API
def call_groq_api(prompt):
    url = "https://api.groq.com/openai/v1/chat/completions"
    
    headers = {
        "Authorization": f"Bearer {groq_api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "llama-2-13b-chat",
        "messages": [{"role": "user", "content": prompt}]
    }
    try:
        response = requests.post(url, headers=headers, json=payload)
        response.raise_for_status()  
        return response.json()['choices'][0]['message']['content']
    except requests.exceptions.HTTPError as err:
        return f"HTTP Error: {err}"
    except Exception as err:
        return f"Error: {err}"

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

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

    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)

    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)

    rewritten = rewrite_requirement_groq(requirement, defects)

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

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

    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
def generate_pdf_report_groq(results):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)

    pdf.set_font("Arial", 'B', 16)
    pdf.cell(200, 10, txt="AI Powered Requirement Analysis - Groq Llama", ln=True, align='C')
    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)

    pdf.set_font("Arial", size=12)
    for i, result in enumerate(results, start=1):
        if pdf.get_y() > 250:
            pdf.add_page()
            pdf.set_font("Arial", 'B', 16)
            pdf.cell(200, 10, txt="AI Powered Requirement Analysis - Groq Llama", 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)

        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)

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

# Streamlit app
def main():
    st.title("AI Requirement Analysis - Groq Llama")
    st.markdown("**Team Name:** Sadia, Areeba, Rabbia, Tesmia")
    st.markdown("**Model:** Groq Llama")

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

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

            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("---")

            pdf_report = generate_pdf_report_groq(results)
            with open(pdf_report, "rb") as f:
                st.download_button(
                    label="Download PDF Report",
                    data=f,
                    file_name="requirements_report_groq.pdf",
                    mime="application/pdf"
                )

if __name__ == "__main__":
    main()