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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
import traceback
# 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 = "" # This will be set from the previous analysis
def set_general_impression(self, impression: str):
print(f"Setting general impression: {impression}")
self.general_impression = impression
def process_message(self, message: str) -> str:
try:
print(f"Interview.process_message received message: {message}")
# Retrieve relevant documents
relevant_docs = qa_chain.invoke({"query": message})
# Generate next question or final report
if self.question_count < 10:
prompt = f"""
You are a Psychologist or a Psychiatrist conducting a clinical interview about the speaker analyzed in the video.
Use the following information to generate the next question:
General Impression from previous analysis: {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 about the speaker.
Important: Your question MUST be a direct follow-up to the most recent answer, while also considering
the entire conversation history and the initial general impression. 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))
print(f"Interview.process_message generated response: {response.content}")
print(f"Type of response: {type(response.content)}")
return response.content
else:
prompt = f"""
Based on the following information, generate a comprehensive clinical report about the speaker
(also make use of technical clinical terms from the provided documents or knowledge base):
General Impression from previous analysis: {self.general_impression}
Context from knowledge base: {relevant_docs}
Full conversation:
{self._format_history()}
Final answer: {message}
Provide a detailed clinical analysis of the speaker based on the initial general impression and the entire interview.
Compare and contrast the insights gained from the interview with the initial general impression.
"""
response = llm.invoke(prompt)
self.question_count = 0 # Reset for next interview
final_report = response.content
self.history = [] # Clear history
print(f"Interview.process_message generated final report: {final_report}")
print(f"Type of final report: {type(final_report)}")
return "Interview complete. Here's the final report:\n\n" + final_report
except Exception as e:
print(f"Error in Interview.process_message: {str(e)}")
print(traceback.format_exc())
return "Error occurred in processing message"
def start_interview(self) -> str:
try:
prompt = f"""
You are a Psychologist or a Psychiatrist starting a clinical interview about the speaker analyzed in the video.
Based on the following information, generate an appropriate opening question:
General Impression from previous analysis: {self.general_impression}
Provide a warm, engaging opening question that sets the tone for the clinical interview and encourages
discussion about the speaker, based on the general impression provided from the previous analysis.
"""
response = llm.invoke(prompt)
opening_question = response.content
print(f"Interview.start_interview generated opening question: {opening_question}")
print(f"Type of opening question: {type(opening_question)}")
return opening_question
except Exception as e:
print(f"Error in Interview.start_interview: {str(e)}")
print(traceback.format_exc())
return "Error occurred in starting interview"
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 |