Update app.py

app.py

@@ -1,20 +1,68 @@
+# app.py
+# Stage 5: Streamlit Application for Resume Screening
+
 import streamlit as st
-from transformers import pipeline
+from transformers import BertTokenizer, BertForSequenceClassification, pipeline
+import torch
+import re
+
+# Set page configuration
+st.set_page_config(page_title="Resume Screening Application", page_icon="📄")
+
+# Title and description
+st.title("Resume Screening Application")
+st.markdown("""
+This application classifies a resume-job pair as **Relevant** or **Irrelevant** and generates a concise summary of the resume's skills.
 
-def main():
-    sentiment_pipeline = pipeline(model="isom5240/2025SpringL2")
+**Classification Criteria**:
+- **Skill Overlap**: At least 80% of the job's required skills must be in the resume.
+- **Experience Match**: The resume's experience must meet or exceed the job's requirement.
+- **Outcome**: Relevant if both conditions are met; otherwise, Irrelevant.
+""")
 
-    st.title("Sentiment Analysis with HuggingFace Spaces")
-    st.write("Enter a sentence to analyze its sentiment:")
+# Load models
+@st.cache_resource
+def load_models():
+    bert_model_path = 'scmlewis/bert-finetuned-isom5240'
+    bert_tokenizer = BertTokenizer.from_pretrained(bert_model_path)
+    bert_model = BertForSequenceClassification.from_pretrained(bert_model_path, num_labels=2)
+    t5_generator = pipeline('text2text-generation', model='t5-small')
+    return bert_tokenizer, bert_model, t5_generator
 
-    user_input = st.text_input("")
-    if user_input:
-        result = sentiment_pipeline(user_input)
-        sentiment = result[0]["label"]
-        confidence = result[0]["score"]
+bert_tokenizer, bert_model, t5_generator = load_models()
 
-        st.write(f"Sentiment: {sentiment}")
-        st.write(f"Confidence: {confidence:.2f}")
+# Input fields
+st.subheader("Enter Resume and Job Description")
+resume = st.text_area("Resume", placeholder="e.g., Skilled in Python, SQL, 3 years experience")
+job_description = st.text_area("Job Description", placeholder="e.g., Data analyst requires Python, SQL, 3 years+")
 
-if __name__ == "__main__":
-    main()
+# Process inputs
+if st.button("Screen Resume"):
+    if resume and job_description:
+        # Classification
+        input_text = f"resume: {resume} [sep] job: {job_description}"
+        inputs = bert_tokenizer(input_text, return_tensors='pt', padding=True, truncation=True, max_length=128)
+        with torch.no_grad():
+            outputs = bert_model(**inputs)
+        suitability = "Relevant" if outputs.logits.argmax().item() == 1 else "Irrelevant"
+        
+        # Summary
+        simplified_resume = re.sub(r'(versed in leveraging|designed applications for|created solutions with|led projects involving|collaborated in agile teams over)', 'proficient in', resume).strip()
+        simplified_resume = re.sub(r'\s+', ' ', simplified_resume)
+        prompt = f"summarize: {simplified_resume}"
+        summary = t5_generator(
+            prompt,
+            max_length=20,
+            min_length=5,
+            num_beams=15,
+            no_repeat_ngram_size=3,
+            length_penalty=0.5,
+            early_stopping=True
+        )[0]['generated_text']
+        
+        # Display results
+        st.subheader("Results")
+        st.write(f"**Suitability**: {suitability}")
+        st.write(f"**Summary**: {summary}")
+    else:
+        st.error("Please enter both a resume and a job description.")