Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,104 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import fitz  # PyMuPDF
+import pandas as pd
+import nltk
+import re
+from sentence_transformers import SentenceTransformer, util
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+import os
+
+# Download NLTK data only if not already downloaded
+nltk_data_dir = os.path.join(os.path.expanduser("~"), "nltk_data")
+nltk.download('punkt', download_dir=nltk_data_dir)
+nltk.download('stopwords', download_dir=nltk_data_dir)
+nltk.data.path.append(nltk_data_dir)
+
+# Set up Streamlit page
+st.set_page_config(page_title="BERT Resume Matcher", layout="wide")
+st.title("🤖 AI Resume Matcher using BERT")
+st.markdown("Upload resumes and a job description — see similarity scores using **semantic NLP** and keyword matching.")
+
+# Function to extract text from a PDF
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Function to extract cleaned keywords from text
+def extract_keywords(text):
+    tokens = word_tokenize(text.lower())
+    stop_words = set(stopwords.words('english'))
+
+    # Custom stopwords (non-skill filler words)
+    custom_stopwords = {
+        'basic', 'knowledge', 'either', 'ctc', 'good', 'lpa', 'per', 'month',
+        'year', 'strong', 'skills', 'required', 'looking', 'fresher',
+        'candidate', 'experience', 'preferred', 'concepts'
+    }
+
+    # Remove non-alphabetic tokens and filter
+    words = [re.sub(r'\W+', '', word) for word in tokens if word.isalpha()]
+    keywords = [word for word in words if word not in stop_words and word not in custom_stopwords and len(word) > 2]
+    
+    return set(keywords)
+
+# Upload UI
+uploaded_files = st.file_uploader("📤 Upload Resumes (PDF)", type="pdf", accept_multiple_files=True)
+job_desc = st.text_area("📝 Paste Job Description Here", height=200)
+
+if st.button("🚀 Match Resumes"):
+    if uploaded_files and job_desc.strip():
+        resume_texts = []
+        resume_names = []
+
+        for file in uploaded_files:
+            try:
+                text = extract_text_from_pdf(file)
+                resume_texts.append(text)
+                resume_names.append(file.name)
+            except Exception as e:
+                st.error(f"❌ Error processing {file.name}: {str(e)}")
+
+        # Load Sentence-BERT model
+        with st.spinner("🔍 Computing similarity..."):
+            model = SentenceTransformer('all-MiniLM-L6-v2')
+
+            # Encode job description and resumes
+            all_docs = [job_desc] + resume_texts
+            embeddings = model.encode(all_docs, convert_to_tensor=True)
+
+            job_embedding = embeddings[0]
+            resume_embeddings = embeddings[1:]
+            semantic_scores = util.cos_sim(job_embedding, resume_embeddings).flatten().tolist()
+
+            # Extract job keywords
+            job_keywords = extract_keywords(job_desc)
+            results = []
+
+            for i in range(len(resume_texts)):
+                resume_keywords = extract_keywords(resume_texts[i])
+                matched = job_keywords & resume_keywords
+                missing = job_keywords - resume_keywords
+                match_ratio = len(matched) / len(job_keywords) if job_keywords else 0
+
+                results.append({
+                    "Resume": resume_names[i],
+                    "Semantic Score (0–100)": round(semantic_scores[i] * 100, 2),
+                    "Skill Match (%)": round(match_ratio * 100, 2),
+                    "Matched Keywords": ", ".join(sorted(matched)),
+                    "Missing Keywords": ", ".join(sorted(missing))
+                })
+
+            results_df = pd.DataFrame(results).sort_values(by="Semantic Score (0–100)", ascending=False).reset_index(drop=True)
+
+        st.success("✅ Matching complete!")
+        st.dataframe(results_df)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        # Download CSV
+        csv = results_df.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download Results as CSV", csv, "resume_match_results.csv", "text/csv")
+    else:
+        st.warning("⚠️ Please upload resumes and enter a job description before matching.")