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Pass eval results to generate_eval_report to avoid redundant calculations #10

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Pass eval results to generate_eval_report to avoid redundant calculations #10

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4 changes: 1 addition & 3 deletions agents/3-llm-loop/functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ def run_all_evals(text, word_count_min=300, word_count_max=800, client_focus_min

return results

def generate_eval_report(profile_text: str) -> str:
def generate_eval_report(profile_text: str, results: dict) -> str:
"""
Generates a markdown-formatted prompt for an LLM to revise an Upwork profile,
with explicit strategies for word count and readability improvements.
Expand All @@ -127,8 +127,6 @@ def generate_eval_report(profile_text: str) -> str:
"""
status_emoji = {True: "✅ Passed", False: "❌ Failed"}

results = run_all_evals(profile_text)

notes = {
"word_count": {
True: "Your profile is within the ideal range of 300 words.",
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4 changes: 2 additions & 2 deletions agents/3-llm-loop/llm-loop-copywriter.ipynb
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Expand Up @@ -202,7 +202,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "c3937b9f-7fe7-41d4-8ac2-1a0e4582cafe",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -234,7 +234,7 @@
" print(results)\n",
"\n",
" # craft new prompt with feedback\n",
" prompt = generate_eval_report(new_profile)\n",
" prompt = generate_eval_report(new_profile, results)\n",
"\n",
" # check if all tests passed\n",
" all_passed = all(results.values())\n",
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