Create app.py

app.py

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+import os
+import streamlit as st
+import pdfplumber
+from fuzzywuzzy import fuzz
+from sklearn.metrics.pairwise import cosine_similarity
+import spacy
+
+# Load the SpaCy model
+#nlp = spacy.load("en_core_web_sm")
+
+import spacy
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    # If the model is not found, download it
+    from spacy.cli import download
+    download("en_core_web_sm")
+    nlp = spacy.load("en_core_web_sm")
+
+
+# Function to extract entities from text
+def extract_entities(text):
+    doc = nlp(text)
+    entities = {ent.label_: ent.text for ent in doc.ents}
+    return entities
+
+# Function to compute matching score
+def compute_advanced_matching_score(cv_text, cv_entities, required_education, required_skills, required_experience):
+    score = 0
+
+    # Named Entity Recognition Matching for Education
+    education = cv_entities.get('EDU', '')
+    score += fuzz.token_set_ratio(education, required_education) / 100
+
+    # Fuzzy Matching for Skills
+    for skill in required_skills:
+        max_skill_match_score = max([fuzz.token_set_ratio(skill, skill_in_cv) for skill_in_cv in cv_text.split()] + [0])
+        score += max_skill_match_score / 100
+
+    # Vector Similarity Matching for Experience
+    experience_text = cv_entities.get('DATE', '')
+    doc1 = nlp(experience_text)
+    doc2 = nlp(f"{required_experience} years")
+    score += cosine_similarity(doc1.vector.reshape(1, -1), doc2.vector.reshape(1, -1))[0][0]
+
+    return score
+
+# Function to process CVs and compute scores
+def process_cvs(uploaded_files, required_education, required_skills, required_experience, top_cvs_count):
+    cv_scores = {}
+    for uploaded_file in uploaded_files:
+        file_extension = uploaded_file.name.split('.')[-1]
+        if file_extension in ["pdf"]:
+            with pdfplumber.open(uploaded_file) as pdf:
+                text = ''
+                for page in pdf.pages:
+                    text += page.extract_text()
+                entities = extract_entities(text)
+                cv_scores[uploaded_file.name] = compute_advanced_matching_score(text, entities, required_education, required_skills, required_experience)
+
+    top_cvs = sorted(cv_scores.items(), key=lambda x: x[1], reverse=True)[:top_cvs_count]
+    return top_cvs
+
+def main():
+    st.markdown('<style>h1{text-align:center;}</style>', unsafe_allow_html=True)  # Center-align the title
+    st.title("Resume Filtering App")
+    
+
+    uploaded_files = st.file_uploader("Upload Resume Files", type=["pdf"], accept_multiple_files=True)
+    required_education = st.text_input("Required Education")
+    required_skills = st.text_input("Required Skills (comma-separated)")
+    required_experience = st.text_input("Required Experience")
+    top_cvs_count = st.number_input("Number of Top Resume to Display", min_value=1, step=1, value=3)
+
+    if st.button("Match Resume"):
+        if uploaded_files and required_education and required_skills and required_experience:
+            required_skills = [skill.strip() for skill in required_skills.split(',')]
+            top_cvs = process_cvs(uploaded_files, required_education, required_skills, required_experience, top_cvs_count)
+            st.subheader("Top Matching Resume:")
+            for filename, score in top_cvs:
+                st.write(f"{filename}: {score:.2f}")
+
+if __name__ == "__main__":
+    main()