Create interview.py

interview.py

@@ -0,0 +1,90 @@
+from langchain.schema import HumanMessage
+from llm_loader import load_model
+from config import openai_api_key
+from processing import combined_retriever, qa_chain
+from typing import Dict, List
+
+# Initialize LLM
+llm = load_model(openai_api_key)
+
+class Interview:
+    def __init__(self):
+        self.question_count = 0
+        self.history: List[tuple] = []
+        self.general_impression: str = ""
+        self.interview_instructions = load_interview_instructions()
+
+    def set_general_impression(self, impression: str):
+        self.general_impression = impression
+
+    def process_message(self, message: str) -> str:
+        # Retrieve relevant documents
+        relevant_docs = qa_chain.invoke({"query": message})
+        
+        # Generate next question or final report
+        if self.question_count < 10:
+            prompt = f"""
+            Your are a Psychologist or a Psychiatrist and conducting a clinical interview.
+            
+            Use the following information to generate the next question:
+            
+            General Impression: {self.general_impression}
+            Context from knowledge base: {relevant_docs}
+            Full conversation history:
+            {self._format_history()}
+            Current answer: {message}
+            Question count: {self.question_count + 1}/10
+
+            Based on all this information, generate the next appropriate question for the clinical interview.
+            Important: Your question MUST be a direct follow-up to the most recent answer, while also considering 
+            the entire conversation history. Ensure that your question builds upon the information just provided 
+            and explores it further or shifts to a related topic based on what was just said.
+            """
+            response = llm.invoke(prompt)
+            self.question_count += 1
+            self.history.append((message, response.content))
+            return response.content
+        else:
+            prompt = f"""
+            Based on the following information, generate a comprehensive clinical report (also make use of technical clinical terms from the provided documents or knowledge base):
+            General Impression: {self.general_impression}
+            Context from knowledge base: {relevant_docs}
+            Full conversation:
+            {self._format_history()}
+            Final answer: {message}
+
+            Provide a detailed clinical analysis based on the entire interview.
+            """
+            response = llm.invoke(prompt)
+            self.question_count = 0  # Reset for next interview
+            final_report = response.content
+            self.history = []  # Clear history
+            return "Interview complete. Here's the final report:\n\n" + final_report
+
+    def start_interview(self) -> str:
+        prompt = f"""
+        Your are a Psychologist or a Psychiatrist and starting a clinical interview.
+        
+        Based on the following information, generate an appropriate opening question:
+        
+        General Impression: {self.general_impression}
+        Interview Instructions: {self.interview_instructions}
+
+        Provide a warm, engaging opening question that sets the tone for the clinical interview and encourages the individual to start sharing about themselves.
+        """
+        response = llm.invoke(prompt)
+        return response.content
+
+    def get_results(self) -> List[tuple]:
+        return self.history
+
+    def _format_history(self) -> str:
+        formatted_history = ""
+        for i, (question, answer) in enumerate(self.history, start=1):
+            formatted_history += f"Q{i}: {question}\nA{i}: {answer}\n\n"
+        return formatted_history
+
+interview = Interview()
+
+def get_interview_instance():
+    return interview