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app.py

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+import streamlit as st
+import joblib
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load the necessary models
+model = joblib.load("resume_classification_model.pkl")
+tokenizer = joblib.load("resume_label_encoder.pkl")
+sbert_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Set up the Streamlit page configuration
+st.set_page_config(page_title="Multi Resume Analyzer", layout="centered")
+st.markdown("<h1 style='text-align: center; color: #4CAF50;'>📝 Multi Resume Analyzer</h1>", unsafe_allow_html=True)
+
+# Input job description
+jd = st.text_input("Enter the Job Description:")
+
+# Initialize session state
+if "text_blocks" not in st.session_state:
+    st.session_state.text_blocks = []
+if "submitted" not in st.session_state:
+    st.session_state.submitted = False
+if "message" not in st.session_state:
+    st.session_state.message = ""
+if "text_input_key" not in st.session_state:
+    st.session_state.text_input_key = 0
+
+# Show textarea and buttons if not submitted
+if not st.session_state.submitted:
+    st.markdown("### ✍️ Enter or Paste Your Resume Below:")
+    text_input = st.text_area("Text Input", height=200, key=f"input_{st.session_state.text_input_key}")
+
+    col1, col2 = st.columns(2)
+    with col1:
+        if st.button("Next"):
+            if text_input.strip():
+                st.session_state.text_blocks.append(text_input.strip())
+                st.session_state.message = f"✅ Resume block {len(st.session_state.text_blocks)} saved!"
+                st.session_state.text_input_key += 1
+                st.rerun()
+            else:
+                st.warning("Please enter some text before clicking Next.")
+    with col2:
+        if st.button("Submit"):
+            if text_input.strip():
+                st.session_state.text_blocks.append(text_input.strip())
+            st.session_state.submitted = True
+            st.rerun()
+
+# Show feedback message after "Next"
+if st.session_state.message:
+    st.success(st.session_state.message)
+
+# After submission
+if st.session_state.submitted and jd:
+    st.markdown("## ✅ Submission Complete!")
+
+    resumes = st.session_state.text_blocks
+
+    # SBERT embeddings
+    embeddings = sbert_model.encode(resumes)  # one vector per resume
+    jd_embedding = sbert_model.encode([jd])   # job description embedding
+
+    # Cosine similarities
+    similarities = cosine_similarity(jd_embedding, embeddings)[0]
+
+    # Predict categories for all resumes
+    predicted_labels = model.predict(embeddings)
+    predicted_categories = tokenizer.inverse_transform(predicted_labels)
+
+    # Create DataFrame
+    df = pd.DataFrame({
+        "Resume": [f"Resume {i+1}" for i in range(len(resumes))],
+        "Index": list(range(len(resumes))),
+        "Score": similarities,
+        "Predicted Category": predicted_categories
+    })
+
+    # Ranking
+    df["Rank"] = df["Score"].rank(ascending=False, method='first').astype(int)
+    df = df.sort_values(by="Rank")
+
+    # Predicted category of top resume
+    top_resume_embedding = embeddings[df.iloc[0]["Index"]].reshape(1, -1)
+    top_pred = model.predict(top_resume_embedding)
+    top_category = tokenizer.inverse_transform(top_pred)[0]
+    st.success(f"🎯 Predicted Job Category for Best Match: **{top_category}**")
+
+    # Display full results table
+    st.markdown("### 📊 Resume Analysis Results:")
+    st.dataframe(df[['Resume', 'Index', 'Score', 'Rank', 'Predicted Category']], width=700)

requirements.txt

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+streamlit==1.32.2
+pandas==2.2.1
+numpy==1.26.4
+scikit-learn==1.4.1.post1
+joblib==1.4.0
+sentence-transformers==2.6.1
+transformers==4.39.3
+torch==2.2.2

resume_classification_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d501ff491efb2c4cedb91c52b232a4702e43023fcc4857648f2e0b1918f549ad
+size 77906

resume_label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a575dafd246993e6ee551491e5b96de44c566db677a33abf48b159cbfbfbd738
+size 633

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aaacc258a637621638a5b72460cc2706498e231bfb55a16f03a02743f5414690
+size 146607