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import json |
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from typing import Dict |
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from langchain import PromptTemplate, LLMChain |
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from models import chat_model |
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final_prompt_template = PromptTemplate( |
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input_variables=["report", "problems", "recommendation"], |
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template=( |
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"Based on the following information:\n" |
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"Report:\n{report}\n\n" |
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"Problem Severity Percentages:\n{problems}\n\n" |
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"Recommended Packages:\n{recommendation}\n\n" |
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"Generate a short summary suitable for video narration that synthesizes this information." |
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) |
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) |
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final_chain = LLMChain(llm=chat_model, prompt=final_prompt_template) |
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def generate_final_summary(report: str, problems: Dict[str, float], recommendation: str) -> str: |
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summary = final_chain.run( |
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report=report, |
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problems=json.dumps(problems), |
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recommendation=recommendation |
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) |
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return summary.strip() |
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shorten_prompt_template = PromptTemplate( |
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input_variables=["final_summary"], |
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template=( |
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"Shorten the following summary to make it concise and engaging for video narration. " |
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"Ensure all key points remain intact:\n\n" |
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"{final_summary}\n\n" |
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"Shortened Summary:" |
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) |
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) |
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shorten_chain = LLMChain(llm=chat_model, prompt=shorten_prompt_template) |
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def shorten_summary(final_summary: str) -> str: |
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shortened = shorten_chain.run(final_summary=final_summary) |
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return shortened.strip() |
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