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app.py

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+import gradio as gr
+import os
+import pdfplumber
+from recommender import rank_resumes, summarize_resume_flan, extract_applicant_name
+
+UPLOAD_FOLDER = "uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+def process_resumes(job_description, uploaded_files):
+    if not job_description.strip():
+        return "Please provide a job description.", None
+
+    # Save uploaded files
+    for file in uploaded_files:
+        filepath = os.path.join(UPLOAD_FOLDER, file.name)
+        with open(filepath, "wb") as f:
+            f.write(file.read())
+
+    # Read resumes
+    resume_texts = []
+    for filename in os.listdir(UPLOAD_FOLDER):
+        filepath = os.path.join(UPLOAD_FOLDER, filename)
+        if filename.endswith(".txt"):
+            with open(filepath, "r", encoding="utf-8") as f:
+                text = f.read()
+        elif filename.endswith(".pdf"):
+            with pdfplumber.open(filepath) as pdf:
+                pages = [page.extract_text() for page in pdf.pages]
+                text = "\n".join(pages)
+        else:
+            continue
+
+        resume_texts.append((filename, text))
+
+    if not resume_texts:
+        return "No valid resumes found. Please upload .txt or .pdf files.", None
+
+    # Rank resumes
+    results = rank_resumes(job_description, resume_texts)
+
+    # Generate summaries
+    for candidate in results:
+        candidate["summary"] = summarize_resume_flan(candidate["text"], job_description)
+
+    # Prepare table
+    table_data = [
+        [candidate.get("applicant_name", extract_applicant_name(candidate["text"])),
+         candidate["filename"],
+         f"{candidate['score']:.4f}",
+         candidate["summary"]]
+        for candidate in results
+    ]
+
+    return "", table_data
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Candidate Recommendation Engine")
+    with gr.Row():
+        job_desc = gr.Textbox(label="Job Description", lines=10, placeholder="Paste job description here...")
+    resumes = gr.File(label="Upload Resumes (.txt or .pdf)", file_types=[".txt", ".pdf"], file_types_display="extensions", file_types_multiple=True)
+    btn = gr.Button("Rank Candidates")
+
+    msg = gr.Markdown()
+    output_table = gr.Dataframe(headers=["Candidate", "File Name", "Similarity Score", "Why a Good Fit"], wrap=True)
+
+    btn.click(process_resumes, inputs=[job_desc, resumes], outputs=[msg, output_table])
+
+if __name__ == "__main__":
+    demo.launch()

recommender.py

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+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+#Embedding Model
+embedder = SentenceTransformer("all-mpnet-base-v2")
+
+#Summarization Model
+model_name = "MBZUAI/LaMini-Flan-T5-248M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+
+device = torch.device("cpu")
+model.to(device)
+
+
+def extract_key_sections(resume_text):
+    sections = {"education": [], "experience": [], "skills": [], "projects": []}
+    lines = resume_text.splitlines()
+    current = None
+
+    for line in lines:
+        line = line.strip()
+        if not line:
+            continue
+
+        l = line.lower()
+        if "education" in l:
+            current = "education"
+        elif "experience" in l or "work history" in l:
+            current = "experience"
+        elif "skills" in l:
+            current = "skills"
+        elif "projects" in l or "certifications" in l:
+            current = "projects"
+        elif current:
+            sections[current].append(line)
+
+    return sections
+
+def extract_applicant_name(resume_text, filename):
+    
+    # first 3 lines
+    lines = resume_text.strip().split("\n")[:3]
+    possible_name = None
+
+    for line in lines:
+        clean_line = line.strip()
+        if clean_line and 2 <= len(clean_line.split()) <= 4:
+            
+            possible_name = clean_line
+            break
+
+    if possible_name:
+        return possible_name
+    return filename.rsplit(".", 1)[0]  #fallback to filename if name not found.
+
+
+def rank_resumes(job_description, resume_texts):
+    if not resume_texts:
+        return []
+
+    texts = [job_description] + [text for _, text in resume_texts]
+    embeddings = embedder.encode(texts)
+    job_embedding = embeddings[0].reshape(1, -1)
+    resume_embeddings = embeddings[1:]
+
+    similarities = cosine_similarity(job_embedding, resume_embeddings)[0]
+
+    results = []
+    for (filename, resume_text), sim in zip(resume_texts, similarities):
+        applicant_name = extract_applicant_name(resume_text, filename)
+        results.append({
+            "filename": filename,                       
+            "applicant_name": applicant_name,            
+            "text": resume_text,
+            "score": round(float(sim), 4)
+        })
+
+    results.sort(key=lambda x: x["score"], reverse=True)
+    return results[:4]
+
+
+# ===== Summarization =====
+def summarize_resume_flan(resume_text, job_description):
+    prompt = f"""
+    Summarize this resume in 3 bullet points, focusing on skills and experience relevant to the job description.
+
+    Job Description:
+    {job_description}
+
+    Resume:
+    {resume_text}
+    """
+
+    inputs = tokenizer(
+        prompt, 
+        return_tensors="pt", 
+        truncation=True, 
+        max_length=512
+    ).to(device)
+
+    outputs = model.generate(
+        **inputs, 
+        max_length=200, 
+        num_beams=4, 
+        early_stopping=True
+    )
+
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)

requirements.txt

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+gradio
+flask
+sentence-transformers
+scikit-learn
+transformers
+accelerate
+torch
+pdfplumber
+