Delete resume.py

resume.py

@@ -1,107 +0,0 @@
-import streamlit as st
-import pdfplumber
-import docx2txt
-import spacy
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import os
-
-# Load the English NLP model from spaCy
-@st.cache_resource
-def load_spacy_model():
-    return spacy.load('en_core_web_sm')
-
-nlp = load_spacy_model()
-
-# Function to extract text from a PDF file
-def extract_text_from_pdf(pdf_file):
-    text = ''
-    with pdfplumber.open(pdf_file) as pdf:
-        for page in pdf.pagesSNS
-            page_text = page.extract_text()
-            if page_text:
-                text += page_text
-    return text
-
-# Function to extract text from a DOCX file
-def extract_text_from_docx(docx_file):
-    return docx2txt.process(docx_file)
-
-# Function to extract user-defined skills from resume text
-def extract_skills(text, user_skills):
-    text = text.lower()
-    extracted = [skill.strip().lower() for skill in user_skills if skill.strip().lower() in text]
-    return list(set(extracted))  # remove duplicates
-
-# Function to estimate years of experience from dates mentioned
-def extract_experience(text):
-    doc = nlp(text)
-    years = []
-    for ent in doc.ents:
-        if ent.label_ == 'DATE':
-            try:
-                if 'year' in ent.text.lower():
-                    num = int(ent.text.split()[0])
-                    years.append(num)
-            except:
-                continue
-    return max(years, default=0)
-
-# Function to compute a similarity score between resume and job description
-def match_score(resume_text, job_description):
-    documents = [resume_text, job_description]
-    tfidf = TfidfVectorizer(stop_words='english')
-    tfidf_matrix = tfidf.fit_transform(documents)
-    score = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])
-    return round(float(score[0][0]) * 100, 2)
-
-# -------- Streamlit Frontend Starts Here -------- #
-
-st.title("🔍 AI Resume Screening App")
-
-# Text area for job description
-job_description = st.text_area("📄 Paste the Job Description Below:", height=200)
-
-# Text input for skills (comma-separated)
-skills_input = st.text_input("🛠️ Enter Required Skills (comma-separated):", placeholder="e.g., Python, SQL, Machine Learning")
-
-# File uploader for multiple resumes
-uploaded_files = st.file_uploader("📂 Upload Resume Files (PDF/DOCX)", type=['pdf', 'docx'], accept_multiple_files=True)
-
-# Main logic to process resumes
-if uploaded_files and job_description and skills_input:
-    # Parse user-entered skills
-    user_skills = [skill.strip() for skill in skills_input.split(',') if skill.strip()]
-    
-    if not user_skills:
-        st.warning("⚠️ Please enter at least one skill.")
-    else:
-        st.markdown("### 🔎 Screening Results")
-
-        # Save uploaded files to /data for persistent storage
-        os.makedirs('/data/resumes', exist_ok=True)
-        for resume in uploaded_files:
-            resume_path = os.path.join('/data/resumes', resume.name)
-            with open(resume_path, 'wb') as f:
-                f.write(resume.read())
-
-            # Extract text
-            if resume.name.endswith('.pdf'):
-                resume_text = extract_text_from_pdf(resume_path)
-            elif resume.name.endswith('.docx'):
-                resume_text = extract_text_from_docx(resume_path)
-            else:
-                st.warning(f"Unsupported file type: {resume.name}")
-                continue
-
-            # Extract information
-            skills = extract_skills(resume_text, user_skills)
-            experience = extract_experience(resume_text)
-            score = match_score(resume_text, job_description)
-
-            # Display results
-            st.subheader(f"👤 Candidate: {resume.name}")
-            st.write(f"✅ **Skills Matched**: {', '.join(skills) if skills else 'None'}")
-            st.write(f"🧠 **Estimated Experience**: {experience} year(s)")
-            st.write(f"📊 **Match Score**: {score}%")
-            st.markdown("---")