Create app_groq.py

app_groq.py

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+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/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()