{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from phoenix.client import Client\n",
    "\n",
    "# Load the existing spans\n",
    "spans_df = Client().spans.get_spans_dataframe(project_name=\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the source of truth\n",
    "dataset_df = pd.read_json(\"../data/metadata.jsonl\", lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter by root agents\n",
    "agents_df = spans_df[(spans_df.span_kind == 'AGENT') & (spans_df.parent_id.isna()) & (spans_df.status_code == 'OK')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pj/v1zrqj1d10x9_1rd2njh_r_r0000gn/T/ipykernel_98186/3107371246.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  agents_df[\"task\"] = agents_df[\"attributes.input.value\"].apply(json.loads).apply(lambda x : x[\"task\"]).str.replace(r'\\s*The mentionned file can be downloaded from.*$', '', regex=True)\n"
     ]
    }
   ],
   "source": [
    "# Retrieve the right question and add the answer\n",
    "agents_df[\"task\"] = agents_df[\"attributes.input.value\"].apply(json.loads).apply(lambda x : x[\"task\"]).str.replace(r'\\s*The mentionned file can be downloaded from.*$', '', regex=True)\n",
    "agents_merged_df = pd.merge(agents_df,dataset_df,how=\"left\",left_on=\"task\", right_on=\"Question\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from phoenix.evals.evaluators import bind_evaluator, async_evaluate_dataframe\n",
    "from phoenix.evals.metrics import exact_match\n",
    "from evaluators import conciseness_evaluator\n",
    "\n",
    "# Define the evaluator\n",
    "exact_match_eval = bind_evaluator(evaluator=exact_match, input_mapping= { \"output\": \"attributes.output.value\", \"expected\": \"Final answer\"})\n",
    "conciseness_evaluator = bind_evaluator(evaluator=conciseness_evaluator, input_mapping={ \"output\": \"attributes.output.value\", \"expected\": \"Final answer\"})\n",
    "results_df = await async_evaluate_dataframe(agents_merged_df, evaluators=[exact_match_eval, conciseness_evaluator])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df[\"exact_match\"] = results_df.exact_match_score.apply(json.loads).apply(lambda x : x[\"score\"])\n",
    "results_df[\"conciseness\"] = results_df.conciseness_evaluator_score.apply(json.loads).apply(lambda x : x[\"label\"])\n",
    "results_df[\"agent_type\"] = results_df[\"attributes.smolagents\"].apply(lambda x : \"multi_agent\" if \"managed_agents\" in x else \"llm_agent\")\n",
    "results_filtered_df = results_df[[\"name\", \"span_kind\", \"start_time\", \"context.span_id\", \"context.trace_id\",\"attributes.output.value\", \"task_id\", \"Question\", \"Final answer\", \"agent_type\", \"exact_match_score\", \"conciseness_evaluator_score\", \"exact_match\", \"conciseness\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/romainfayoux/Documents/Programmation/Final_Assignment_Template/.venv/lib/python3.12/site-packages/phoenix/evals/utils.py:367: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  result_df = pd.concat(result_dfs, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# Upload results\n",
    "import numpy as np\n",
    "from phoenix.evals.utils import to_annotation_dataframe\n",
    "\n",
    "annotation_df = to_annotation_dataframe(results_filtered_df)\n",
    "annotation_df = annotation_df.replace({np.nan: None})\n",
    "Client().spans.log_span_annotations_dataframe(dataframe=annotation_df)\n"
   ]
  }
 ],
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