Delete Main_App

Main_App/app.py

@@ -1,69 +0,0 @@
-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()

Main_App/recommender.py

@@ -1,112 +0,0 @@
-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)

Main_App/requirements.txt

@@ -1,9 +0,0 @@
-gradio
-flask
-sentence-transformers
-scikit-learn
-transformers
-accelerate
-torch
-pdfplumber
-