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from utils.llm_client import llm_client, llm_so_client |
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from pydantic import BaseModel, Field |
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class Score(BaseModel): |
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score: int |
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def assign_certainty_score(st): |
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print(f"\nassign certainty score: {st.session_state.state.discrepancies}\n") |
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text = "## A. Certainty Score\n" |
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index = 0 |
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for item in st.session_state.state.discrepancies: |
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index += 1 |
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system_prompt = "You are a compliance analyst." |
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user_messages = ( |
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f"Assess the following discrepancy and assign a certainty score from 0 to 100 indicating how certain you are that this is a real issue.\n\n" |
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f"**Discrepancy:**\n{item['discrepancy']}\n\n" |
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"Provide the certainty score as a single number." |
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) |
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item['certainty_score'] = llm_so_client(system_prompt, user_messages, responseModel=Score) |
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text += f"1. System prompt:\n```\n{system_prompt}\n```\n" |
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text += f"2. User message:\n```\n{user_messages}\n```\n" |
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text += f"3. Score:\n```\n {item['certainty_score']}\n```\n" |
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return text |
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def assign_severity_score(st): |
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print(f"\nassign severity score: {st.session_state.state.discrepancies}\n") |
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text = "## B. Severity Score\n" |
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index = 0 |
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for item in st.session_state.state.discrepancies: |
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index += 1 |
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system_prompt = "You are a risk assessment expert." |
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user_messages = ( |
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f"Given the following discrepancy, assign a severity score from 0 to 100 where 0 is not severe and 100 is very severe.\n\n" |
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f"**Discrepancy:**\n{item['discrepancy']}\n\n" |
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"Provide the severity score as a single number." |
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) |
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item['severity_score'] = llm_so_client(system_prompt, user_messages, responseModel=Score) |
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text += f"### DISCREPANCIE {index}\n" |
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text += f"1. System prompt:\n```\n{system_prompt}\n```\n" |
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text += f"2. User message:\n```\n{user_messages}\n```\n" |
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text += f"3. Score:\n```\n {item['certainty_score']}\n```\n" |
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return text |
