Create model.py

model.py

@@ -0,0 +1,157 @@
+import os
+import fitz
+from docx import Document
+from sentence_transformers import SentenceTransformer, util
+from transformers import pipeline
+import pandas as pd
+from datetime import datetime
+import zipfile
+import shutil
+import re
+import imaplib
+import email
+
+# ================== MODELS ==================
+MODELS = {
+    "Fast (MiniLM)": "sentence-transformers/all-MiniLM-L6-v2",
+    "Balanced (Recommended)": "sentence-transformers/all-mpnet-base-v2",
+    "High Accuracy": "sentence-transformers/multi-qa-mpnet-base-dot-v1"
+}
+
+loaded_models = {}
+skills_classifier = None
+
+def get_model(name):
+    if name not in loaded_models:
+        loaded_models[name] = SentenceTransformer(MODELS[name])
+    return loaded_models[name]
+
+def get_classifier():
+    global skills_classifier
+    if skills_classifier is None:
+        skills_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+    return skills_classifier
+
+# ================== FILE READER ==================
+def extract_text(file_path):
+    ext = os.path.splitext(file_path)[1].lower()
+
+    try:
+        if ext == ".pdf":
+            doc = fitz.open(file_path)
+            text = "\n".join([p.get_text() for p in doc])
+            doc.close()
+            return text.strip()
+
+        elif ext in [".docx", ".doc"]:
+            doc = Document(file_path)
+            return "\n".join([p.text for p in doc.paragraphs]).strip()
+
+    except:
+        return ""
+
+    return ""
+
+# ================== GMAIL FETCH ==================
+def fetch_from_gmail(email_user, app_password):
+    mail = imaplib.IMAP4_SSL("imap.gmail.com")
+    mail.login(email_user, app_password)
+    mail.select("inbox")
+
+    result, data = mail.search(None, '(SUBJECT "resume")')
+    ids = data[0].split()
+
+    files = []
+
+    for i in ids[-10:]:
+        result, msg_data = mail.fetch(i, "(RFC822)")
+        msg = email.message_from_bytes(msg_data[0][1])
+
+        for part in msg.walk():
+            if part.get_content_disposition() == "attachment":
+                filename = part.get_filename()
+
+                if filename:
+                    path = f"temp_{filename}"
+                    with open(path, "wb") as f:
+                        f.write(part.get_payload(decode=True))
+                    files.append(path)
+
+    return files
+
+# ================== AI FEATURES ==================
+def extract_skills(text):
+    labels = ["Python", "Machine Learning", "Deep Learning", "SQL", "AWS", "Docker", "Communication"]
+
+    try:
+        clf = get_classifier()
+        res = clf(text[:2000], labels, multi_label=True)
+        return ", ".join([l for l, s in zip(res["labels"], res["scores"]) if s > 0.4])
+    except:
+        return "N/A"
+
+def extract_qualifications(text):
+    pattern = r'\b(bba|bs|bsc|ba|mba|msc|phd|bachelor|master)\b'
+    found = re.findall(pattern, text.lower())
+    return ", ".join(set(found)).upper() if found else "Not mentioned"
+
+# ================== MAIN FUNCTION ==================
+def screen_resumes(job_desc, files, model_name="Fast (MiniLM)", threshold=0.65,
+                   gmail=None, password=None):
+
+    # Gmail integration
+    if gmail and password:
+        files = fetch_from_gmail(gmail, password)
+
+    model = get_model(model_name)
+    job_emb = model.encode(job_desc, convert_to_tensor=True)
+
+    results = []
+
+    os.makedirs("outputs", exist_ok=True)
+
+    for f in files:
+        # safe file handling
+        if hasattr(f, "read"):
+            name = f.name
+            path = f"temp_{name}"
+            with open(path, "wb") as x:
+                x.write(f.read())
+            fpath = path
+        else:
+            fpath = f
+            name = os.path.basename(f)
+
+        text = extract_text(fpath)
+
+        if len(text) < 50:
+            continue
+
+        emb = model.encode(text, convert_to_tensor=True)
+        score = util.cos_sim(job_emb, emb)[0][0].item()
+
+        status = "Shortlisted" if score >= threshold else "Rejected"
+
+        results.append({
+            "Candidate": name,
+            "Score": round(score * 100, 2),
+            "Skills": extract_skills(text),
+            "Qualification": extract_qualifications(text),
+            "Status": status
+        })
+
+    df = pd.DataFrame(results)
+    report_path = f"outputs/report_{datetime.now().strftime('%Y%m%d_%H%M')}.csv"
+    df.to_csv(report_path, index=False)
+
+    zip_path = None
+    shortlisted = df[df["Status"] == "Shortlisted"]
+
+    if len(shortlisted) > 0:
+        zip_path = "outputs/shortlisted.zip"
+        with zipfile.ZipFile(zip_path, "w") as z:
+            for f in files:
+                if os.path.exists(f):
+                    z.write(f)
+
+    return results, report_path, zip_path