Upload 3 files

.streamlit/config.toml

@@ -0,0 +1,2 @@
+[theme]
+base="light"

app.py

@@ -0,0 +1,250 @@
+import streamlit as st
+from streamlit_chat import message
+from langchain_google_genai import ChatGoogleGenerativeAI
+import os
+import PyPDF2
+import docx
+import requests
+from bs4 import BeautifulSoup
+import re
+
+# Set up Gemini AI
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyBZ92WbBwz0pWqHPjjT2lqGrwlAfM91Rds")
+chat_model = ChatGoogleGenerativeAI(model="gemini-1.5-pro", api_version="v1",
+    google_api_key=GEMINI_API_KEY)
+
+# Set page config
+st.set_page_config(page_title="AI-Driven Job Assistant", layout="wide")
+
+# Initialize session state for messages if not set
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if "resume_text" not in st.session_state:
+    st.session_state.resume_text = ""
+
+# Extract text from resume
+def extract_text_from_resume(resume_file):
+    text = ""
+    if resume_file.name.endswith(".pdf"):
+        pdf_reader = PyPDF2.PdfReader(resume_file)
+        for page in pdf_reader.pages:
+            extracted_text = page.extract_text()
+            if extracted_text:
+                text += extracted_text + "\n"
+    elif resume_file.name.endswith(".docx"):
+        doc = docx.Document(resume_file)
+        for para in doc.paragraphs:
+            text += para.text + "\n"
+    return text.strip()
+
+# Sidebar resume upload
+with st.sidebar:
+    st.header("Upload Your Resume")
+    resume = st.file_uploader("Upload PDF or DOCX", type=["pdf", "docx"])
+    if resume:
+        st.success("Resume uploaded successfully!")
+        if st.button("Proceed"):
+            with st.spinner("Analyzing your resume..."):
+                resume_text = extract_text_from_resume(resume)
+                if resume_text:
+                    st.session_state.resume_text = resume_text
+                    ats_prompt = f"Analyze this resume and provide an ATS score (out of 100) along with improvement suggestions keep the response in short:\n\n{resume_text}"
+                    ats_response = chat_model.predict(ats_prompt)
+                    st.session_state.messages.append({"text": ats_response, "is_user": False})
+                    st.session_state["resume_analyzed"] = True  # Mark resume as analyzed
+                    st.rerun()
+                else:
+                    st.error("Could not extract text from the uploaded resume.")
+
+# Main chat interface
+st.title("💬 AI-Driven Job Assist")
+st.subheader("ATS insights & Chatbot Assist")
+
+# Display chat messages
+for msg in st.session_state.messages:
+    message(msg["text"], is_user=msg["is_user"])
+
+# Ask for aspired job role after ATS score
+if "resume_analyzed" in st.session_state and "aspired job role" not in st.session_state:
+    ai_response = "What is your aspired job role?"
+    st.session_state.messages.append({"text": ai_response, "is_user": False})
+    del st.session_state["resume_analyzed"]  # Remove flag after asking
+    st.rerun()
+
+# Input area
+if "user_message" not in st.session_state:
+    st.session_state.user_message = ""
+
+
+user_input = st.text_input("Type your message here...", key="user_message")
+
+if st.button("Send") and user_input:
+    st.session_state.messages.append({"text": user_input, "is_user": True})
+    
+    # AI response logic
+    if "aspired job role" not in st.session_state:
+        st.session_state["aspired job role"] = user_input
+        job_role = st.session_state["aspired job role"]
+        skills_prompt = f"For the job role of {job_role}, suggest the essential skills and any missing skills based on the user's resume:\n\nResume:\n{st.session_state.resume_text}\n\nAlso, provide relevant learning resources for upskilling with valid links of courses.keep the response in short"
+        ai_response = chat_model.predict(skills_prompt)
+    
+    st.session_state.messages.append({"text": ai_response, "is_user": False})
+    st.session_state.pop("user_message", None)
+    st.rerun()
+
+# Fetch jobs from LinkedIn
+def fetch_jobs_from_linkedin(keyword, location, max_results=5):
+    search_url = f"https://www.linkedin.com/jobs/search?keywords={keyword}&location={location}"
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    }
+    
+    response = requests.get(search_url, headers=headers)
+    if response.status_code != 200:
+        st.error("Failed to fetch jobs from LinkedIn. Try again later.")
+        return []
+    
+    soup = BeautifulSoup(response.text, "html.parser")
+    job_listings = []
+    
+    for job_card in soup.find_all("div", class_="base-card")[:max_results]:
+        title_tag = job_card.find("h3", class_="base-search-card__title")
+        company_tag = job_card.find("h4", class_="base-search-card__subtitle")
+        location_tag = job_card.find("span", class_="job-search-card__location")
+        link_tag = job_card.find("a", class_="base-card__full-link")
+        
+        if title_tag and company_tag and location_tag and link_tag:
+            job_listings.append({
+                "title": title_tag.text.strip(),
+                "company": company_tag.text.strip(),
+                "location": location_tag.text.strip(),
+                "link": link_tag["href"].strip(),
+                "recruiter_email": fetch_recruiter_email(job_card)  # Placeholder function
+            })
+    
+    return job_listings
+
+# Placeholder function for fetching recruiter email
+def fetch_recruiter_email(job_card):
+    # Replace this with actual logic to find recruiter emails
+    email_tag = job_card.find("span", class_="recruiter-email")  # Example selector
+    return email_tag.text.strip() if email_tag else None
+
+# Function to generate cold email and cover letter
+def generate_email_and_cover_letter(job_title, company, recruiter_email):
+    cold_email = f"""
+    Subject: Application for {job_title} Position at {company}
+    
+    Dear Hiring Manager,
+    
+    I am excited to apply for the {job_title} position at {company}. With my skills and experience, I believe I am a great fit for this role. I have attached my resume for your reference and would love the opportunity to discuss further.
+    
+    Looking forward to your response.
+    
+    Best Regards,
+    [Your Name]
+    """
+    
+    cover_letter = f"""
+    Dear Hiring Manager,
+    
+    I am writing to express my interest in the {job_title} position at {company}. I have a strong background in [mention relevant skills] and believe my expertise aligns with the job requirements.
+    
+    I am eager to bring my skills to your esteemed company and contribute effectively. Please find my resume attached for review.
+    
+    Thank you for your time and consideration. I look forward to the opportunity to speak with you.
+    
+    Sincerely,
+    [Your Name]
+    """
+    
+    return cold_email, cover_letter
+
+# Function to generate LinkedIn message & connection request note
+def generate_linkedin_outreach(job_title, company):
+    linkedin_message = f"""
+    Hi [Recruiter Name],
+
+    I hope you're doing well. I came across the {job_title} opening at {company} and I am very interested. 
+    I would love to connect and learn more about this opportunity. 
+    Looking forward to your response!
+
+    Best,  
+    [Your Name]
+    """
+
+    connection_request_note = f"""
+    Hi [Recruiter Name], I’m interested in the {job_title} role at {company} and would love to connect.
+    """
+
+    return linkedin_message, connection_request_note
+
+# Streamlit UI
+st.title("📌 Job Listings from LinkedIn")
+keyword = st.text_input("Job Title (e.g., Data Scientist)", value=st.session_state.get("aspired job role", ""))
+location = st.text_input("Location (e.g., New York)")
+
+if st.button("Fetch Jobs"):
+    jobs = fetch_jobs_from_linkedin(keyword, location)
+    if jobs:
+        for job in jobs[:3]:
+            st.markdown(f"**{job['title']}** at {job['company']} ({job['location']})")
+            st.markdown(f"[Apply Now]({job['link']})")
+
+            # Call Gemini to get match score
+            match_prompt = f"""Based on the following job role and resume, provide a match score out of 100 indicating how well the resume fits the job. Also give a 1-line reason for the score.
+            Job Role: {job['title']} at {job['company']} in {job['location']}
+            
+            Resume: {st.session_state.resume_text}
+            Keep the response short and structured like this:
+            Score: 85
+            Reason: Strong experience in React and REST APIs aligns well.
+            """
+            match_response = chat_model.predict(match_prompt)
+            
+            # Display the match score. Extract score and reason from the response
+            match = re.search(r"Score:\s*(\d+)\s*\nReason:\s*(.*)", match_response)
+            if match:
+                score = int(match.group(1))
+                reason = match.group(2)
+                
+                # Use color coding for different match levels
+                if score >= 80:
+                    color = "green"
+                elif score >= 50:
+                    color = "orange"
+                else:
+                    color = "red"
+                    
+                # Display match score with highlight
+                st.markdown(f"🔍 **Resume Match Score:** <span style='color:{color}; font-size:22px; font-weight:bold'>{score}/100</span>", unsafe_allow_html=True)
+                # Display reason separately
+                st.markdown(f"📌 **Reason:** {reason}")
+            else:
+                st.markdown("⚠️ Could not extract match score. Please check the response format.")
+
+
+            
+            # Show recruiter email if available
+            if job['recruiter_email']:
+                st.markdown(f"**Recruiter Email:** {job['recruiter_email']}")
+                cold_email, cover_letter = generate_email_and_cover_letter(job['title'], job['company'], job['recruiter_email'])
+                
+                with st.expander("📧 Suggested Cold Email"):
+                    st.code(cold_email)
+                
+                with st.expander("📜 Suggested Cover Letter"):
+                    st.code(cover_letter)
+            else:
+                st.markdown(f"**Recruiters Email not available**")
+
+                # Provide LinkedIn message & connection note
+                linkedin_msg, connection_note = generate_linkedin_outreach(job['title'], job['company'])
+                
+                with st.expander("💬 Suggested LinkedIn Message"):
+                    st.code(linkedin_msg)
+                
+                with st.expander("🔗 Connection Request Note"):
+                    st.code(connection_note)
+    else:
+        st.write("No jobs found. Try a different search.")

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+streamlit_chat
+langchain-google-genai
+PyPDF2
+python-docx
+requests
+beautifulsoup4
+python-dotenv