app.py

import streamlit as st
import requests
from fpdf import FPDF
import os
import time
from datetime import datetime
import groq
import pandas as pd
import matplotlib.pyplot as plt

# API keys (replace with your keys or use environment variables)
mistral_api_key = os.getenv("MISTRAL_API_KEY", "gz6lDXokxgR6cLY72oomALWcm7vhjRzQ")
groq_api_key = os.getenv("GROQ_API_KEY", "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")

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

# Function to call Mistral API
def call_mistral_api(prompt):
    start_time = time.time()
    url = "https://api.mistral.ai/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {mistral_api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "mistral-medium",
        "messages": [
            {"role": "user", "content": prompt}
        ]
    }
    try:
        response = requests.post(url, headers=headers, json=payload)
        response.raise_for_status()  # Raise an error for bad status codes
        end_time = time.time()
        speed = end_time - start_time
        content = response.json()['choices'][0]['message']['content']
        return content, speed
    except requests.exceptions.HTTPError as err:
        if response.status_code == 429:  # Rate limit exceeded
            st.warning("Rate limit exceeded. Please wait a few seconds and try again.")
            time.sleep(5)  # Wait for 5 seconds before retrying
            return call_mistral_api(prompt)  # Retry the request
        return f"HTTP Error: {err}", 0
    except Exception as err:
        return f"Error: {err}", 0

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

# Function to analyze a single requirement using both models
def analyze_requirement(requirement):
    # Use Mistral for classification and domain identification
    type_prompt = f"Classify the following requirement as Functional or Non-Functional in one word:\n\n{requirement}\n\nType:"
    req_type, type_speed = call_mistral_api(type_prompt)
    req_type = req_type.strip()

    domain_prompt = f"Classify the domain for the following requirement in one word (e.g., E-commerce, Education, etc.):\n\n{requirement}\n\nDomain:"
    domain, domain_speed = call_mistral_api(domain_prompt)
    domain = domain.strip()

    # Use Groq for defect analysis and rewriting
    defects_prompt = f"""List ONLY the major defects in the following requirement (e.g., Ambiguity, Incompleteness, etc.) in 1-2 words each:\n\n{requirement}\n\nDefects:"""
    defects, defects_speed = call_groq_api(defects_prompt)
    defects = defects.strip()

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

    # Calculate total speed for this requirement
    total_speed = type_speed + domain_speed + defects_speed + rewritten_speed

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

# Function to calculate accuracy (placeholder logic)
def calculate_accuracy(results):
    # Placeholder logic: Assume 80% accuracy for demonstration
    # Replace this with actual logic if you have correct answers
    return 80.0  # Return accuracy as a percentage

# Function to generate a PDF report
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", 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):
        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", 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"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=f"Total Speed: {result['Total_Speed']:.2f}s", 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")
    st.markdown("**Team Name:** Sadia, Areeba, Rabbia, Tesmia")
    st.markdown("**Models:** Mistral (Classification & Domain) + Groq (Defects & Rewriting)")

    # Input requirements manually
    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()]

    # Analyze requirements
    if st.button("Analyze Requirements"):
        if not requirements:
            st.warning("Please enter requirements.")
        else:
            results = []
            total_speed = 0
            for req in requirements:
                if req.strip():  # Ignore empty lines
                    result = analyze_requirement(req.strip())
                    results.append(result)
                    total_speed += result["Total_Speed"]

            # Calculate accuracy (placeholder logic)
            accuracy = calculate_accuracy(results)

            # Display total speed and accuracy
            st.subheader("Performance Metrics")
            col1, col2 = st.columns(2)
            with col1:
                st.metric("Total Speed", f"{total_speed:.2f} seconds")
            with col2:
                st.metric("Accuracy", f"{accuracy:.2f}%")

            # Visualize metrics
            st.subheader("Visualization")
            metrics_data = {
                "Metric": ["Total Speed", "Accuracy"],
                "Value": [total_speed, accuracy]
            }
            df = pd.DataFrame(metrics_data)

            # Bar chart
            fig, ax = plt.subplots()
            ax.bar(df["Metric"], df["Value"], color=["blue", "green"])
            ax.set_ylabel("Value")
            ax.set_title("Performance Metrics")
            st.pyplot(fig)

            # Table
            st.table(df)

            # 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()