Create app.py

app.py

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+import streamlit as st
+from pathlib import Path
+from typing import List, Optional
+from llama_index.llms.base import LLM
+from llama_index.llms import OpenAI
+from llama_index.readers import PDFReader
+from llama_index.llama_pack.base import BaseLlamaPack
+from llama_index.schema import NodeWithScore
+from llama_index.response_synthesizers import TreeSummarize
+from pydantic import BaseModel, Field
+
+QUERY_TEMPLATE = """
+You are an expert resume reviewer. 
+Your job is to decide if the candidate passes the resume screen given the job description and a list of criteria:
+
+### Job Description
+{job_description}
+
+### Screening Criteria
+{criteria_str}
+"""
+
+# Define the BaseModel and other classes you provided
+
+
+# Function to format criteria list into a string
+def _format_criteria_str(criteria: List[str]) -> str:
+    criteria_str = ""
+    for criterion in criteria:
+        criteria_str += f"- {criterion}\n"
+    return criteria_str
+
+
+class ResumeScreenerPack(BaseLlamaPack):
+    # ... (your existing code for ResumeScreenerPack)
+
+
+def main():
+    st.title("Resume Screener")
+
+    job_description = st.text_area("Job Description")
+    criteria = st.text_area("Screening Criteria (Separate by new lines)")
+
+    criteria_list = criteria.split('\n') if criteria else []
+
+    def run_resume_screener(resume_path):
+        screener = ResumeScreenerPack(job_description, criteria_list)
+        output = screener.run(resume_path)
+        st.json(output)  # Displaying JSON output for example
+
+    uploaded_file = st.file_uploader("Upload a resume", type=['pdf'])
+
+    if uploaded_file:
+        st.write("Resume uploaded!")
+        run_resume_screener(uploaded_file)
+
+if __name__ == "__main__":
+    main()