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app.py

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+import streamlit as st
+import pdfplumber
+import torch
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+# Load Hugging Face model
+model_name = "deepset/roberta-base-squad2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+qa_pipeline = pipeline(
+    "question-answering",
+    model=model,
+    tokenizer=tokenizer,
+    truncation=True,
+    max_length=1024
+)
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    with pdfplumber.open(pdf_file) as pdf:
+        text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
+        return text if text.strip() else "No text found in the document."
+
+# Function to extract information using LLM
+def extract_info(text, question):
+    if not text or len(text.strip()) < 50:
+        return "Insufficient text for analysis."
+    if not question or len(question.strip()) == 0:
+        return "No valid question provided."
+
+    try:
+        result = qa_pipeline({
+            "context": text[:1024],
+            "question": question
+        })
+        return result.get("answer", "No relevant information found.")
+    except Exception as e:
+        return f"Error processing request: {str(e)}"
+
+# Function to analyze resume vs job description
+def analyze_resume_vs_job(resume_text, job_desc):
+    job_keywords = set(job_desc.lower().split())  # Extract keywords from JD
+    resume_keywords = set(resume_text.lower().split())  # Extract keywords from resume
+
+    matched_skills = job_keywords.intersection(resume_keywords)  # Common words
+    missing_skills = job_keywords.difference(resume_keywords)  # Missing words
+
+    return matched_skills, missing_skills
+
+# Streamlit UI
+st.title("📄 AI-Powered ATS Resume Parser")
+st.write("Upload a resume to extract key details automatically!")
+
+uploaded_file = st.file_uploader("Upload Resume (PDF or TXT)", type=["pdf", "txt"])
+
+if uploaded_file is not None:
+    file_extension = uploaded_file.name.split(".")[-1]
+    if file_extension == "pdf":
+        resume_text = extract_text_from_pdf(uploaded_file)
+    else:
+        resume_text = uploaded_file.read().decode("utf-8")
+
+    st.subheader("📌 Extracted Resume Information")
+    name = extract_info(resume_text, "What is the applicant's name?")
+    job_title = extract_info(resume_text, "What is the applicant's job title?")
+    skills = extract_info(resume_text, "What are the applicant's skills?")
+    experience = extract_info(resume_text, "How many years of experience does the applicant have?")
+
+    st.write(f"**🧑 Name:** {name}")
+    st.write(f"**💼 Job Title:** {job_title}")
+    st.write(f"**🛠 Skills:** {skills}")
+    st.write(f"**📅 Experience:** {experience} years")
+
+    # Job Matching (Enhanced)
+    st.subheader("🔍 Match Resume with Job Description")
+    job_desc = st.text_area("Paste the Job Description Here:")
+
+    if st.button("Match Resume"):
+        if not job_desc.strip():
+            st.warning("⚠️ Please enter a Job Description before matching.")
+        else:
+            match_score = extract_info(resume_text, f"How well does this resume match the job description: {job_desc}")
+            matched_skills, missing_skills = analyze_resume_vs_job(resume_text, job_desc)
+
+            st.write(f"**✅ Match Score:** {match_score}")
+            st.write(f"**✔ Matched Skills:** {', '.join(matched_skills) if matched_skills else 'None'}")
+            st.write(f"**❌ Missing Skills:** {', '.join(missing_skills) if missing_skills else 'None'}")

reruirements.txt

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+streamlit
+transformers
+torch
+pdfplumber