Create app.py

app.py

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+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    return model
+
+def calculate_similarity(model, text1, text2):
+    embedding1 = model.encode([text1])
+    embedding2 = model.encode([text2])
+    return cosine_similarity(embedding1, embedding2)[0][0]
+
+st.title("Resume Matcher")
+
+model = load_model()
+
+jd = st.text_area("Enter the Job Description:", height=200)
+resume = st.text_area("Enter the Resume:", height=200)
+
+if st.button("Calculate Match Score"):
+    if jd and resume:
+        score = calculate_similarity(model, jd, resume)
+        st.write(f"The match score is: {score}")
+    else:
+        st.write("Please enter both the job description and resume.")