Update app.py

app.py

@@ -1,4 +1,201 @@
 import streamlit as st
+import requests
+import json
+import webbrowser
+from io import StringIO
+from groq import Groq
+from bs4 import BeautifulSoup
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Initialize session state
+if 'original_resume' not in st.session_state:
+    st.session_state['original_resume'] = None
+if 'keywords' not in st.session_state:
+    st.session_state['keywords'] = None
+if 'tailored_resume' not in st.session_state:
+    st.session_state['tailored_resume'] = None
+
+def scrape_website(url):
+    response = requests.get(url)
+    response.raise_for_status()
+    soup = BeautifulSoup(response.text, 'html.parser')
+    return soup.get_text()
+
+def extract_keywords(job_description, client):
+    completion = client.chat.completions.create(
+        model="llama-3.1-70b-versatile",
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are an expert in extracting essential information from job postings for optimal ATS compatibility. "
+                    "Focus on identifying keywords and skills, prioritized by importance."
+                )
+            },
+            {
+                "role": "user",
+                "content": (
+                    f"Extract keywords from this job posting and categorize them by importance. "
+                    f"Return as JSON with exactly these keys: 'high', 'medium', and 'low' containing arrays of strings.\n\n{job_description}"
+                )
+            }
+        ],
+        temperature=1,
+        max_tokens=4096,
+        response_format={"type": "json_object"}
+    )
+    return json.loads(completion.choices[0].message.content)
+
+def adapt_resume(resume_data, keywords, job_description, client):
+    completion = client.chat.completions.create(
+        model="llama-3.1-8b-instant",
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are a CV coach skilled in resume customization and JSON formatting. "
+                    "Tailor the resume to emphasize relevant keywords while maintaining factual accuracy."
+                )
+            },
+            {
+                "role": "user",
+                "content": f"Keywords: {json.dumps(keywords)}\nResume: {json.dumps(resume_data)}\nJob Description: {job_description}"
+            }
+        ],
+        temperature=0.9,
+        max_tokens=8000,
+        response_format={"type": "json_object"}
+    )
+    return json.loads(completion.choices[0].message.content)
+
+def calculate_resume_match(resume_data, keywords):
+    """Calculate match score between resume and keywords"""
+    resume_text = json.dumps(resume_data).lower()
+    total_score = 0
+    matches = {'high': [], 'medium': [], 'low': []}
+    
+    # Weight multipliers for different priority levels
+    weights = {"high": 3, "medium": 2, "low": 1}
+    
+    # Ensure keywords has the expected structure
+    if not all(key in keywords for key in ['high', 'medium', 'low']):
+        raise ValueError("Keywords must contain 'high', 'medium', and 'low' arrays")
+    
+    for priority in ['high', 'medium', 'low']:
+        priority_score = 0
+        priority_matches = []
+        
+        for word in keywords[priority]:
+            word = word.lower()
+            if word in resume_text:
+                priority_score += weights[priority]
+                priority_matches.append(word)
+                
+        matches[priority] = priority_matches
+        total_score += priority_score
+    
+    # Normalize score to 0-100
+    max_possible = sum(len(keywords[p]) * weights[p] for p in ['high', 'medium', 'low'])
+    normalized_score = (total_score / max_possible * 100) if max_possible > 0 else 0
+    
+    return normalized_score, matches
+
+# Page config
+st.set_page_config(page_title="Resume Tailor", page_icon="📄", layout="wide")
+
+# Header
+st.title("🎯 AI Resume Tailor")
+st.markdown("### Transform your resume for your dream job")
+
+# Sidebar with API key
+with st.sidebar:
+    api_key = st.text_input(
+        "Groq API Key", 
+        type="password",
+        help="Get your API key at https://console.groq.com/keys"
+    )
+    if not api_key:
+        st.markdown("[Get API Key](https://console.groq.com/keys)")
+
+# Main input section
+col1, col2 = st.columns(2)
+with col1:
+    job_url = st.text_input("Job Posting URL", placeholder="https://...")
+with col2:
+    resume_file = st.file_uploader("Upload Resume (JSON)", type="json")
+    if resume_file:
+        resume_str = StringIO(resume_file.getvalue().decode("utf-8"))
+        st.session_state['original_resume'] = json.load(resume_str)
+
+# Process button
+if st.button("🚀 Tailor Resume", type="primary", use_container_width=True):
+    if job_url and api_key and resume_file:
+        try:
+            with st.status("🔄 Processing...") as status:
+                # Initialize client
+                client = Groq(api_key=api_key)
+                
+                # Scrape and process
+                status.update(label="Analyzing job posting...")
+                job_description = scrape_website(job_url)
+                keywords = extract_keywords(job_description, client)
+                st.session_state['keywords'] = keywords
+                
+                status.update(label="Tailoring resume...")
+                tailored_resume = adapt_resume(
+                    st.session_state['original_resume'],
+                    keywords,
+                    job_description,
+                    client
+                )
+                st.session_state['tailored_resume'] = tailored_resume
+                status.update(label="✅ Done!", state="complete")
+
+            # Results section
+            st.markdown("---")
+            st.markdown("### 📊 Results")
+            
+            # Calculate and display scores
+            original_score, original_matches = calculate_resume_match(
+                st.session_state['original_resume'],
+                st.session_state['keywords']
+            )
+            tailored_score, tailored_matches = calculate_resume_match(
+                st.session_state['tailored_resume'],
+                st.session_state['keywords']
+            )
+
+            score_col1, score_col2 = st.columns(2)
+            with score_col1:
+                st.metric("Original Match", f"{original_score:.1f}%")
+            with score_col2:
+                st.metric("Tailored Match", f"{tailored_score:.1f}%", 
+                         delta=f"+{tailored_score - original_score:.1f}%")
+
+            # Keyword matches
+            with st.expander("🎯 View Keyword Matches"):
+                for priority in ['high', 'medium', 'low']:
+                    st.subheader(f"{priority.title()} Priority")
+                    orig_matches = set(original_matches[priority])
+                    new_matches = set(tailored_matches[priority])
+                    added = new_matches - orig_matches
+                    
+                    st.write("✓ Original:", ", ".join(orig_matches) if orig_matches else "None")
+                    if added:
+                        st.write("✚ Added:", f"<span style='background-color: #d4edda;'>{', '.join(added)}</span>", unsafe_allow_html=True)
+
+            # Download section
+            st.markdown("### 📥 Download")
+            if st.download_button(
+                "⬇️ Download Tailored Resume",
+                data=json.dumps(st.session_state['tailored_resume'], indent=4),
+                file_name="tailored_resume.json",
+                mime="application/json",
+                use_container_width=True
+            ):
+                webbrowser.open_new_tab("https://rxresu.me/")
+                st.info("📝 Opening Resume Builder in new tab...")
+
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+    else:
+        st.error("Please provide all required inputs")