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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)
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