Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 from pathlib import Path
-from typing import List, Optional
+from typing import Any, Dict, List, Optional
 from llama_index.llms.base import LLM
 from llama_index.llms import OpenAI
 from llama_index.readers import PDFReader
@@ -20,10 +20,28 @@ Your job is to decide if the candidate passes the resume screen given the job de
 {criteria_str}
 """
 
-# Define the BaseModel and other classes you provided
+
+class CriteriaDecision(BaseModel):
+    """The decision made based on a single criterion"""
+
+    decision: bool = Field(description="The decision made based on the criterion")
+    reasoning: str = Field(description="The reasoning behind the decision")
+
+
+class ResumeScreenerDecision(BaseModel):
+    """The decision made by the resume screener"""
+
+    criteria_decisions: List[CriteriaDecision] = Field(
+        description="The decisions made based on the criteria"
+    )
+    overall_reasoning: str = Field(
+        description="The reasoning behind the overall decision"
+    )
+    overall_decision: bool = Field(
+        description="The overall decision made based on the criteria"
+    )
 
 
-# Function to format criteria list into a string
 def _format_criteria_str(criteria: List[str]) -> str:
     criteria_str = ""
     for criterion in criteria:
@@ -32,7 +50,35 @@ def _format_criteria_str(criteria: List[str]) -> str:
 
 
 class ResumeScreenerPack(BaseLlamaPack):
-    # ... (your existing code for ResumeScreenerPack)
+    def __init__(
+        self,
+        job_description: str,
+        criteria: List[str],
+        llm: Optional[LLM] = None,
+    ) -> None:
+        self.reader = PDFReader()
+        llm = llm or OpenAI(model="gpt-4")
+        service_context = ServiceContext.from_defaults(llm=llm)
+        self.synthesizer = TreeSummarize(
+            output_cls=ResumeScreenerDecision, service_context=service_context
+        )
+        criteria_str = _format_criteria_str(criteria)
+        self.query = QUERY_TEMPLATE.format(
+            job_description=job_description, criteria_str=criteria_str
+        )
+
+    def get_modules(self) -> Dict[str, Any]:
+        """Get modules."""
+        return {"reader": self.reader, "synthesizer": self.synthesizer}
+
+    def run(self, resume_path: str, *args: Any, **kwargs: Any) -> Any:
+        """Run pack."""
+        docs = self.reader.load_data(Path(resume_path))
+        output = self.synthesizer.synthesize(
+            query=self.query,
+            nodes=[NodeWithScore(node=doc, score=1.0) for doc in docs],
+        )
+        return output.response
 
 
 def main():