{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "source": [ "%%capture\n", "\n", "!pip install -q tabulate fpdf2" ], "metadata": { "id": "cdvLOdcZnzaX" }, "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "source": [ "GPU check" ], "metadata": { "id": "nfy6rq76HdiO" } }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nxjR8SCn3L5U", "outputId": "f39a6f53-6781-4204-ab89-7309af30031c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "python : 3.12.13\n", "torch : 2.11.0+cu128\n", "transformers: 5.13.1\n", "datasets : 4.0.0\n", "cuda avail : True\n", "gpu : Tesla T4 | 15.6 GB | compute 7.5\n", "dtype : torch.float16\n" ] } ], "source": [ "import os, warnings, logging, sys\n", "os.environ[\"HF_HUB_DISABLE_PROGRESS_BARS\"] = \"1\"\n", "os.environ[\"TRANSFORMERS_VERBOSITY\"] = \"error\"\n", "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", "warnings.filterwarnings(\"ignore\")\n", "logging.getLogger(\"huggingface_hub\").setLevel(logging.ERROR)\n", "\n", "import torch, transformers\n", "transformers.utils.logging.disable_progress_bar()\n", "transformers.utils.logging.set_verbosity_error()\n", "\n", "\n", "try:\n", " import datasets\n", " datasets.logging.set_verbosity_error()\n", " DATASETS_VERSION = datasets.__version__\n", "except Exception as e:\n", " DATASETS_VERSION = f\"unavailable ({type(e).__name__})\"\n", "\n", "from packaging.version import parse as V\n", "assert V(transformers.__version__) >= V(\"4.45\"), \\\n", " f\"transformers too old: {transformers.__version__}\"\n", "\n", "print(\"python :\", sys.version.split()[0])\n", "print(\"torch :\", torch.__version__)\n", "print(\"transformers:\", transformers.__version__)\n", "print(\"datasets :\", DATASETS_VERSION)\n", "print(\"cuda avail :\", torch.cuda.is_available())\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"No GPU. Runtime > Change runtime type > T4 GPU, then re-run.\")\n", "\n", "gpu = torch.cuda.get_device_name(0)\n", "vram = torch.cuda.get_device_properties(0).total_memory / 1e9\n", "cc = torch.cuda.get_device_capability(0)\n", "print(f\"gpu : {gpu} | {vram:.1f} GB | compute {cc[0]}.{cc[1]}\")\n", "\n", "\n", "DTYPE = torch.bfloat16 if cc[0] >= 8 else torch.float16\n", "print(\"dtype :\", DTYPE)" ] }, { "cell_type": "markdown", "source": [ "Data layer" ], "metadata": { "id": "K_4lZRqR4UdV" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from huggingface_hub import HfApi, hf_hub_download\n", "\n", "TABULAR_EXT = (\".csv\", \".tsv\", \".parquet\", \".json\", \".jsonl\")\n", "\n", "\n", "def _read_any(path: str) -> pd.DataFrame:\n", " \"\"\"Dispatch to the right pandas reader based on extension.\"\"\"\n", " if path.endswith(\".parquet\"):\n", " return pd.read_parquet(path)\n", " if path.endswith(\".tsv\"):\n", " return pd.read_csv(path, sep=\"\\t\")\n", " if path.endswith((\".json\", \".jsonl\")):\n", " return pd.read_json(path, lines=path.endswith(\".jsonl\"))\n", " return pd.read_csv(path)\n", "\n", "\n", "def load_hf_dataframe(hf_dataset: str) -> pd.DataFrame:\n", " \"\"\"Hub dataset id -> DataFrame. Tries load_dataset, falls back to raw files.\n", "\n", " The fallback matters: datasets>=3.0 dropped script-based repos, so datasets\n", " such as mstz/titanic can no longer be loaded the standard way.\n", " \"\"\"\n", "\n", " try:\n", " from datasets import load_dataset\n", " ds = load_dataset(hf_dataset)\n", " split = \"train\" if \"train\" in ds else list(ds.keys())[0]\n", " print(f\"[loader] load_dataset OK split='{split}'\")\n", " return ds[split].to_pandas()\n", " except Exception as e:\n", " print(f\"[loader] load_dataset failed -> {type(e).__name__}: {str(e)[:120]}\")\n", "\n", "\n", " files = HfApi().list_repo_files(hf_dataset, repo_type=\"dataset\")\n", " cands = [f for f in files if f.lower().endswith(TABULAR_EXT)]\n", " if not cands:\n", " raise RuntimeError(f\"No tabular file found in {hf_dataset}. Files: {files}\")\n", "\n", "\n", " cands.sort(key=lambda f: ((\"train\" not in f.lower()), len(f)))\n", " pick = cands[0]\n", " print(f\"[loader] fallback -> downloading '{pick}'\")\n", " df = _read_any(hf_hub_download(hf_dataset, pick, repo_type=\"dataset\"))\n", " print(\"[loader] fallback OK\")\n", " return df\n", "\n", "\n", "\n", "hf_dataset = \"mstz/titanic\"\n", "df = load_hf_dataframe(hf_dataset)\n", "print(\"\\nshape:\", df.shape)\n", "display(df.head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 323 }, "id": "Yk6csvF_4TpC", "outputId": "ad114027-a69e-4d0f-9cc3-6c4cb9fbd46a" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[loader] load_dataset failed -> RuntimeError: Dataset scripts are no longer supported, but found titanic.py\n", "[loader] fallback -> downloading 'titanic.csv'\n", "[loader] fallback OK\n", "\n", "shape: (891, 12)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " has_survived passenger_class surname \\\n", "0 0 3 'Braund' \n", "1 1 1 'Cumings' \n", "2 1 3 'Heikkinen' \n", "3 1 1 'Futrelle' \n", "4 0 3 'Allen' \n", "\n", " name sex age sibsp parch \\\n", "0 'Mr. Owen Harris' male 22 1 0 \n", "1 'Mrs. John Bradley (Florence Briggs Thayer)' female 38 1 0 \n", "2 'Miss. Laina' female 26 0 0 \n", "3 'Mrs. Jacques Heath (Lily May Peel)' female 35 1 0 \n", "4 'Mr. William Henry' male 35 0 0 \n", "\n", " ticket fare cabin embarked \n", "0 'A/5 21171' 7.2500 '' S \n", "1 'PC 17599' 71.2833 C85 C \n", "2 'STON/O2. 3101282' 7.9250 '' S \n", "3 113803 53.1000 C123 S \n", "4 373450 8.0500 '' S " ], "text/html": [ "\n", "
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has_survivedpassenger_classsurnamenamesexagesibspparchticketfarecabinembarked
003'Braund''Mr. Owen Harris'male2210'A/5 21171'7.2500''S
111'Cumings''Mrs. John Bradley (Florence Briggs Thayer)'female3810'PC 17599'71.2833C85C
213'Heikkinen''Miss. Laina'female2600'STON/O2. 3101282'7.9250''S
311'Futrelle''Mrs. Jacques Heath (Lily May Peel)'female351011380353.1000C123S
403'Allen''Mr. William Henry'male35003734508.0500''S
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"display(df\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"has_survived\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"passenger_class\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 3,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"surname\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"'Cumings'\",\n \"'Allen'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"'Mrs. John Bradley (Florence Briggs Thayer)'\",\n \"'Mr. William Henry'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"female\",\n \"male\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"38\",\n \"35\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sibsp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"parch\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ticket\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"'PC 17599'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fare\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 30.5100288352535,\n \"min\": 7.25,\n \"max\": 71.2833,\n \"num_unique_values\": 5,\n \"samples\": [\n 71.2833\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cabin\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"''\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"embarked\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "t7TNU1hS4yWu" }, "execution_count": 18, "outputs": [] }, { "cell_type": "markdown", "source": [ "\tNormalize and Context card" ], "metadata": { "id": "y8d6DQ_u4yMA" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "\n", "\n", "def normalize(df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"Strip literal quote chars, turn empty strings into real NaN, re-infer numerics.\"\"\"\n", " df = df.copy()\n", " for c in df.select_dtypes(include=\"object\").columns:\n", " s = df[c].astype(str).str.strip()\n", " s = s.str.replace(r\"^'(.*)'$\", r\"\\1\", regex=True)\n", " df[c] = s.str.strip().replace(\n", " {\"\": np.nan, \"nan\": np.nan, \"None\": np.nan, \"?\": np.nan})\n", "\n", "\n", " for c in df.select_dtypes(include=\"object\").columns:\n", " conv = pd.to_numeric(df[c], errors=\"coerce\")\n", " if conv.isna().sum() == df[c].isna().sum():\n", " df[c] = conv\n", " return df\n", "\n", "\n", "def build_context(df: pd.DataFrame, name: str) -> str:\n", " \"\"\"Compact, model-facing description of the table. One line per column.\"\"\"\n", " lines = [f\"Dataset: {name}\",\n", " f\"Shape: {df.shape[0]} rows x {df.shape[1]} columns\", \"\", \"Columns:\"]\n", " for c in df.columns:\n", " s, nulls = df[c], df[c].isna().sum()\n", " if pd.api.types.is_numeric_dtype(s):\n", " detail = f\"min={s.min():.4g} max={s.max():.4g} mean={s.mean():.4g}\"\n", " else:\n", " detail = \"examples=\" + \", \".join(map(str, s.dropna().unique()[:4]))[:70]\n", " lines.append(f\"- {c} ({s.dtype}) nulls={nulls} ({100*nulls/len(df):.1f}%) \"\n", " f\"unique={s.nunique(dropna=True)} {detail}\")\n", " lines += [\"\", \"First 3 rows:\", df.head(3).to_string(max_colwidth=22)]\n", " return \"\\n\".join(lines)\n", "\n", "\n", "\n", "df = normalize(df)\n", "context = build_context(df, hf_dataset)\n", "print(context)\n", "print(f\"\\n[context card: {len(context)} chars, ~{len(context)//4} tokens]\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sUplGji342PF", "outputId": "9bcd3952-c193-4191-f791-b265b63528d6" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset: mstz/titanic\n", "Shape: 891 rows x 12 columns\n", "\n", "Columns:\n", "- has_survived (int64) nulls=0 (0.0%) unique=2 min=0 max=1 mean=0.3838\n", "- passenger_class (int64) nulls=0 (0.0%) unique=3 min=1 max=3 mean=2.309\n", "- surname (object) nulls=0 (0.0%) unique=667 examples=Braund, Cumings, Heikkinen, Futrelle\n", "- name (object) nulls=0 (0.0%) unique=803 examples=Mr. Owen Harris, Mrs. John Bradley (Florence Briggs Thayer), Miss. Lai\n", "- sex (object) nulls=0 (0.0%) unique=2 examples=male, female\n", "- age (float64) nulls=177 (19.9%) unique=88 min=0.42 max=80 mean=29.7\n", "- sibsp (int64) nulls=0 (0.0%) unique=7 min=0 max=8 mean=0.523\n", "- parch (int64) nulls=0 (0.0%) unique=7 min=0 max=6 mean=0.3816\n", "- ticket (object) nulls=0 (0.0%) unique=681 examples=A/5 21171, PC 17599, STON/O2. 3101282, 113803\n", "- fare (float64) nulls=0 (0.0%) unique=248 min=0 max=512.3 mean=32.2\n", "- cabin (object) nulls=687 (77.1%) unique=147 examples=C85, C123, E46, G6\n", "- embarked (object) nulls=2 (0.2%) unique=3 examples=S, C, Q\n", "\n", "First 3 rows:\n", " has_survived passenger_class surname name sex age sibsp parch ticket fare cabin embarked\n", "0 0 3 Braund Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S\n", "1 1 1 Cumings Mrs. John Bradley ... female 38.0 1 0 PC 17599 71.2833 C85 C\n", "2 1 3 Heikkinen Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S\n", "\n", "[context card: 1541 chars, ~385 tokens]\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "9d7nOXMR5pYx" }, "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "source": [ "Model layer" ], "metadata": { "id": "HuSyzeIF5nE1" } }, { "cell_type": "code", "source": [ "from transformers import (AutoTokenizer, AutoModelForCausalLM,\n", " StoppingCriteria, StoppingCriteriaList)\n", "\n", "MODEL_ID = \"Qwen/Qwen2.5-Coder-1.5B-Instruct\"\n", "\n", "tok = AutoTokenizer.from_pretrained(MODEL_ID)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " dtype=DTYPE,\n", " device_map=\"cuda\",\n", ")\n", "model.eval()\n", "if tok.pad_token_id is None:\n", " tok.pad_token = tok.eos_token\n", "\n", "print(f\"loaded {MODEL_ID}\")\n", "print(f\"params : {model.num_parameters()/1e9:.2f}B\")\n", "print(f\"vram : {torch.cuda.memory_allocated()/1e9:.2f} GB\")\n", "\n", "\n", "class StopOnClosingFence(StoppingCriteria):\n", " \"\"\"Stop once a fenced block has been opened and closed.\n", "\n", " Decoding the tail is more reliable than matching token ids: ``` tokenizes\n", " differently depending on what precedes it. Decoding on every step would be\n", " wasteful, so we only check every `check_every` tokens.\n", " \"\"\"\n", "\n", " def __init__(self, tokenizer, prompt_len, fences=2, check_every=8):\n", " self.tok, self.prompt_len = tokenizer, prompt_len\n", " self.fences, self.check_every = fences, check_every\n", " self._step = 0\n", "\n", " def __call__(self, input_ids, scores, **kwargs):\n", " self._step += 1\n", " done = False\n", " if self._step % self.check_every == 0:\n", " text = self.tok.decode(input_ids[0][self.prompt_len:],\n", " skip_special_tokens=True)\n", " done = text.count(\"```\") >= self.fences\n", "\n", " return torch.full((input_ids.shape[0],), done,\n", " dtype=torch.bool, device=input_ids.device)\n", "\n", "\n", "@torch.inference_mode()\n", "def generate(system: str, user: str, max_new_tokens: int = 900,\n", " stop_fences: int = 0) -> str:\n", " \"\"\"Single entry point to the LLM. Returns only the newly generated text.\n", "\n", " stop_fences=2 halts as soon as the closing ``` of a code block is emitted,\n", " instead of running on to max_new_tokens with trailing prose.\n", " \"\"\"\n", " msgs = [{\"role\": \"system\", \"content\": system},\n", " {\"role\": \"user\", \"content\": user}]\n", " text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)\n", " inputs = tok(text, return_tensors=\"pt\").to(model.device)\n", " prompt_len = inputs[\"input_ids\"].shape[1]\n", "\n", " criteria = None\n", " if stop_fences:\n", " criteria = StoppingCriteriaList(\n", " [StopOnClosingFence(tok, prompt_len, fences=stop_fences)])\n", "\n", " out = model.generate(\n", " **inputs,\n", " max_new_tokens=max_new_tokens,\n", " do_sample=False,\n", " pad_token_id=tok.pad_token_id,\n", " stopping_criteria=criteria,\n", " )\n", " return tok.decode(out[0][prompt_len:], skip_special_tokens=True)\n", "\n", "\n", "\n", "print(\"\\n--- smoke test ---\")\n", "print(generate(\"You are a Python expert. Reply with code only.\",\n", " \"Write a one-line pandas snippet that counts nulls per column of df.\",\n", " max_new_tokens=80, stop_fences=2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QYjSHWAO5m9l", "outputId": "373d692d-cc77-4f9f-ea08-c6a654fed1e2" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "loaded Qwen/Qwen2.5-Coder-1.5B-Instruct\n", "params : 1.54B\n", "vram : 3.13 GB\n", "\n", "--- smoke test ---\n", "```python\n", "import pandas as pd\n", "\n", "# Assuming df is your DataFrame\n", "null_counts = df.isna().sum()\n", "print(null_counts)\n", "```\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "OQl5d0zZ7KsF" }, "execution_count": 20, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "cOiYKS637MuH" }, "execution_count": 20, "outputs": [] }, { "cell_type": "markdown", "source": [ "Code generation" ], "metadata": { "id": "_zrmlV497OPO" } }, { "cell_type": "code", "source": [ "import re\n", "import matplotlib\n", "matplotlib.use(\"Agg\")\n", "\n", "PLOTS_DIR = \"plots\"\n", "os.makedirs(PLOTS_DIR, exist_ok=True)\n", "\n", "CODE_SYSTEM = \"\"\"You are a data analyst. You write Python code for exploratory data analysis.\n", "\n", "Rules:\n", "- A pandas DataFrame named `df` is already loaded. Never load, download or create data.\n", "- Use pandas, matplotlib and seaborn.\n", "- print() every result you want reported.\n", "- Save each figure with plt.savefig(\"plots/.png\", bbox_inches=\"tight\") then plt.close(). Never call plt.show().\n", "- Reply with exactly one ```python code block and no other text.\"\"\"\n", "\n", "\n", "def build_code_prompt(context: str, instruction: str) -> str:\n", " return f\"{context}\\n\\nTask: {instruction}\"\n", "\n", "\n", "def extract_code(text: str) -> str:\n", " \"\"\"Pull python out of markdown fences; concatenate if the model emitted several.\"\"\"\n", " blocks = re.findall(r\"```(?:python|py)?\\s*\\n(.*?)```\", text, flags=re.DOTALL)\n", " return \"\\n\\n\".join(b.strip() for b in blocks) if blocks else text.strip()" ], "metadata": { "id": "Eq3p8tD97SCx" }, "execution_count": 21, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "2nsCYFoW9AuA" }, "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "source": [ "Execution Sandbox" ], "metadata": { "id": "39zFwbrW9BYU" } }, { "cell_type": "code", "source": [ "import io, traceback, contextlib, glob\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import FuncFormatter, LogLocator, NullFormatter, ScalarFormatter\n", "\n", "BANNED = re.compile(r\"\\b(pd\\.read_\\w+|load_dataset|sns\\.load_dataset)\\s*\\(\")\n", "CORR_FIX = re.compile(r\"\\.corr\\(\\s*\\)\")\n", "\n", "MAX_ANNOT_COLS = 12\n", "MAX_TICKS = 20\n", "MAX_CAT_LEVELS = 20\n", "SKIP_ATTR = \"_edagent_skip\"\n", "TITLE_ATTR = \"_edagent_title\"\n", "\n", "\n", "def sanitize(code: str) -> str:\n", " \"\"\"Neutralize data reloads; patch the one error the model can't reliably fix.\"\"\"\n", " out = []\n", " for line in code.splitlines():\n", " if BANNED.search(line):\n", " out.append(\"# [removed by agent] \" + line.strip())\n", " else:\n", " out.append(CORR_FIX.sub(\".corr(numeric_only=True)\", line))\n", " return \"\\n\".join(out)\n", "\n", "\n", "def _short(s, n=18):\n", " s = str(s)\n", " return s if len(s) <= n else f\"{s[:n//2 - 1]}…{s[-(n//2 - 1):]}\"\n", "\n", "\n", "def _tick(v, _=None):\n", " \"\"\"Plain numbers with thousands separators — never 1e7.\"\"\"\n", " return f\"{v:,.0f}\" if abs(v) >= 1000 else f\"{v:g}\"\n", "\n", "\n", "def _tidy(fig):\n", " \"\"\"Make any figure legible: thin dense ticks, rotate, kill scientific notation.\"\"\"\n", " for ax in fig.get_axes():\n", " if len(ax.get_xticklabels()) > MAX_TICKS:\n", " step = max(1, len(ax.get_xticks()) // MAX_TICKS)\n", " ax.set_xticks(ax.get_xticks()[::step])\n", " for axis, scale in ((ax.xaxis, ax.get_xscale()), (ax.yaxis, ax.get_yscale())):\n", "\n", " if scale == \"linear\" and isinstance(axis.get_major_formatter(), ScalarFormatter):\n", " axis.set_major_formatter(FuncFormatter(_tick))\n", " plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", fontsize=8)\n", " plt.setp(ax.get_yticklabels(), fontsize=8)\n", "\n", "\n", "def _install_plot_guards():\n", " \"\"\"Patch the real modules, so `import matplotlib.pyplot as plt` can't bypass us.\"\"\"\n", " real = {\"savefig\": plt.savefig, \"heatmap\": sns.heatmap, \"histplot\": sns.histplot,\n", " \"boxplot\": sns.boxplot, \"violinplot\": sns.violinplot,\n", " \"countplot\": sns.countplot}\n", "\n", "\n", " def _vars(a, kw):\n", " \"\"\"Recover the (name, Series) pairs a seaborn call is plotting.\"\"\"\n", " d = kw.get(\"data\", a[0] if a else None)\n", " out = []\n", " for key in (\"x\", \"y\"):\n", " v = kw.get(key)\n", " if isinstance(v, str) and hasattr(d, \"columns\"):\n", " out.append((v, d[v]))\n", " elif isinstance(v, pd.Series):\n", " out.append((v.name, v))\n", " if not out and isinstance(d, pd.Series):\n", " out.append((d.name, d))\n", " return out\n", "\n", " def _counts_instead(name, s):\n", " \"\"\"A box plot of labels is meaningless; show how often each level occurs.\"\"\"\n", " ax = plt.gca()\n", " s.value_counts().head(MAX_CAT_LEVELS).sort_values().plot(kind=\"barh\", ax=ax)\n", " ax.set_xlabel(\"count\")\n", " ax.set_ylabel(str(name or \"\"))\n", " setattr(plt.gcf(), TITLE_ATTR, f\"Counts of {name}\" if name else \"Counts\")\n", " return ax\n", "\n", " def _categorical_guard(fn_name, a, kw):\n", " \"\"\"Return an axes if we handled it, else None to fall through.\"\"\"\n", " pairs = _vars(a, kw)\n", " if not pairs or any(pd.api.types.is_numeric_dtype(s) for _, s in pairs):\n", " return None\n", " name, s = pairs[0]\n", " if s.nunique(dropna=True) > MAX_CAT_LEVELS:\n", " setattr(plt.gcf(), SKIP_ATTR, True)\n", " return plt.gca()\n", " return _counts_instead(name, s)\n", "\n", "\n", " def savefig(fname, *a, **kw):\n", " fig = plt.gcf()\n", " if getattr(fig, SKIP_ATTR, False):\n", " return None\n", " title = getattr(fig, TITLE_ATTR, None)\n", " if title and fig.get_axes():\n", " fig.get_axes()[0].set_title(title)\n", " w, h = fig.get_size_inches()\n", " fig.set_size_inches(max(w, 8), max(h, 5))\n", " _tidy(fig)\n", " kw.setdefault(\"bbox_inches\", \"tight\")\n", " kw.setdefault(\"dpi\", 110)\n", " return real[\"savefig\"](fname, *a, **kw)\n", "\n", " def heatmap(data, *a, **kw):\n", " n = getattr(data, \"shape\", (0, 0))[1]\n", " if n > MAX_ANNOT_COLS:\n", " kw[\"annot\"] = False\n", " kw.setdefault(\"cmap\", \"coolwarm\")\n", " side = min(max(7, 0.5 * n + 4), 20)\n", " plt.gcf().set_size_inches(side, side * 0.85)\n", " ax = real[\"heatmap\"](data, *a, **kw)\n", " if hasattr(data, \"columns\"):\n", " fs = 8 if n <= 15 else 6\n", " ax.set_xticklabels([_short(c, 24) for c in data.columns],\n", " rotation=45, ha=\"right\", fontsize=fs)\n", " ax.set_yticklabels([_short(c, 24) for c in data.index],\n", " rotation=0, fontsize=fs)\n", " return ax\n", "\n", " def _numeric_series(a, kw):\n", " pairs = _vars(a, kw)\n", " for _, s in pairs:\n", " s = pd.to_numeric(s, errors=\"coerce\").dropna()\n", " if len(s):\n", " return s\n", " return None\n", "\n", " def histplot(*a, **kw):\n", " s = _numeric_series(a, kw)\n", " logged = False\n", "\n", " if (s is not None and len(s) > 20 and s.min() > 0\n", " and s.max() / max(s.median(), 1e-9) > 50 and \"log_scale\" not in kw):\n", " kw[\"log_scale\"] = (True, False)\n", " logged = True\n", " ax = real[\"histplot\"](*a, **kw)\n", " if logged:\n", " ax.xaxis.set_major_locator(LogLocator(base=10, subs=(1.0, 2.0, 5.0),\n", " numticks=12))\n", " ax.xaxis.set_major_formatter(FuncFormatter(_tick))\n", " ax.xaxis.set_minor_formatter(NullFormatter())\n", " ax.set_xlabel(f\"{ax.get_xlabel()} (log scale)\")\n", " return ax\n", "\n", " def boxplot(*a, **kw):\n", " handled = _categorical_guard(\"boxplot\", a, kw)\n", " return handled if handled is not None else real[\"boxplot\"](*a, **kw)\n", "\n", " def violinplot(*a, **kw):\n", " handled = _categorical_guard(\"violinplot\", a, kw)\n", " return handled if handled is not None else real[\"violinplot\"](*a, **kw)\n", "\n", " def countplot(*a, **kw):\n", " pairs = _vars(a, kw)\n", " if pairs and pairs[0][1].nunique(dropna=True) > MAX_CAT_LEVELS:\n", " setattr(plt.gcf(), SKIP_ATTR, True)\n", " return plt.gca()\n", " return real[\"countplot\"](*a, **kw)\n", "\n", " plt.savefig = savefig\n", " sns.heatmap, sns.histplot = heatmap, histplot\n", " sns.boxplot, sns.violinplot, sns.countplot = boxplot, violinplot, countplot\n", " return real\n", "\n", "\n", "def run_code(code: str, df: pd.DataFrame) -> dict:\n", " \"\"\"Execute code with df in scope. Return stdout, error traceback, new plot paths.\"\"\"\n", " before = set(glob.glob(f\"{PLOTS_DIR}/*.png\"))\n", " ns = {\"df\": df.copy(), \"pd\": pd, \"np\": np, \"plt\": plt, \"sns\": sns}\n", "\n", " real = _install_plot_guards()\n", " buf, err = io.StringIO(), None\n", " try:\n", " with contextlib.redirect_stdout(buf):\n", " exec(code, ns)\n", " except Exception:\n", " err = traceback.format_exc(limit=3)\n", " finally:\n", "\n", " for i, num in enumerate(plt.get_fignums(), start=1):\n", " fig = plt.figure(num)\n", " if fig.get_axes() and not getattr(fig, SKIP_ATTR, False):\n", " fig.savefig(f\"{PLOTS_DIR}/plot_{i}.png\")\n", " plt.close(\"all\")\n", " plt.savefig = real[\"savefig\"]\n", " sns.heatmap, sns.histplot = real[\"heatmap\"], real[\"histplot\"]\n", " sns.boxplot, sns.violinplot = real[\"boxplot\"], real[\"violinplot\"]\n", " sns.countplot = real[\"countplot\"]\n", "\n", " return {\"ok\": err is None, \"stdout\": buf.getvalue(), \"error\": err,\n", " \"plots\": sorted(set(glob.glob(f\"{PLOTS_DIR}/*.png\")) - before)}" ], "metadata": { "id": "3nt72BDt9B1N" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "o78yiDBj9fL7" }, "execution_count": 22, "outputs": [] }, { "cell_type": "markdown", "source": [ "Repair loop" ], "metadata": { "id": "awuIQZiE9fen" } }, { "cell_type": "code", "source": [ "import shutil\n", "\n", "FIX_SYSTEM = \"\"\"You fix broken Python data-analysis code.\n", "\n", "Rules:\n", "- A pandas DataFrame named `df` is already loaded. Never load or create data.\n", "- Return the complete corrected script, not just the changed lines.\n", "- Reply with exactly one ```python code block and no other text.\"\"\"\n", "\n", "\n", "def format_error(err: str, code: str) -> str:\n", " \"\"\"Turn a harness traceback into a signal about the model's own code.\n", "\n", " exec'd code has no source file, so Python prints the failing line number\n", " with no source text. We reconstruct it from the code we sent.\n", " \"\"\"\n", " last = err.strip().splitlines()[-1]\n", " hits = re.findall(r'File \"\", line (\\d+)', err)\n", " if not hits:\n", " return last\n", " ln, src = int(hits[-1]), code.splitlines()\n", " line = src[ln - 1].strip() if 0 < ln <= len(src) else \"\"\n", " return f\"Line {ln}: {line}\\n{last}\"\n", "\n", "\n", "def generate_and_run(context: str, instruction: str, df: pd.DataFrame,\n", " retries: int = 1) -> dict:\n", " shutil.rmtree(PLOTS_DIR, ignore_errors=True)\n", " os.makedirs(PLOTS_DIR, exist_ok=True)\n", "\n", " code = sanitize(extract_code(\n", " generate(CODE_SYSTEM, build_code_prompt(context, instruction),\n", " max_new_tokens=1200, stop_fences=2)))\n", " res = run_code(code, df)\n", " print(f\"[attempt 1] ok={res['ok']}\")\n", "\n", " for attempt in range(retries):\n", " if res[\"ok\"]:\n", " break\n", " fix_prompt = (f\"{context}\\n\\nThis code failed:\\n```python\\n{code}\\n```\\n\\n\"\n", " f\"Error:\\n{format_error(res['error'], code)}\\n\\n\"\n", " f\"Return the corrected script.\")\n", " code = sanitize(extract_code(\n", " generate(FIX_SYSTEM, fix_prompt, max_new_tokens=1200, stop_fences=2)))\n", " res = run_code(code, df)\n", " print(f\"[attempt {attempt + 2}] ok={res['ok']}\")\n", "\n", " res[\"code\"] = code\n", " res[\"plots\"] = sorted(glob.glob(f\"{PLOTS_DIR}/*.png\"))\n", " return res" ], "metadata": { "id": "v8SQyOnopNHh" }, "execution_count": 23, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "a0Weuau-qPZP" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "synthesis layer" ], "metadata": { "id": "KVlfXPNrHuER" } }, { "cell_type": "code", "source": [ "REDUNDANT_R = 0.99\n", "MAX_GROUP_BLOCKS = 10\n", "\n", "\n", "def fmt_num(v):\n", " \"\"\"Plain, comma-separated numbers. Never 1.02011e+06.\"\"\"\n", " if pd.isna(v):\n", " return \"\"\n", " v = float(v)\n", " if v.is_integer():\n", " return f\"{int(v):,}\"\n", " if abs(v) >= 1000:\n", " return f\"{v:,.0f}\"\n", " return f\"{v:,.2f}\"\n", "\n", "\n", "def schema_only(df: pd.DataFrame, name: str) -> str:\n", " \"\"\"Context minus sample rows, so stray values can't be copied into prose.\"\"\"\n", " return (f\"Dataset: {name}\\nShape: {df.shape[0]} rows x {df.shape[1]} columns\\n\"\n", " \"Columns: \" + \", \".join(f\"{c} ({df[c].dtype})\" for c in df.columns))\n", "\n", "\n", "def corr_pairs(df: pd.DataFrame):\n", " \"\"\"Split correlations into real findings and near-duplicate columns.\"\"\"\n", " num = df.select_dtypes(include=\"number\")\n", " if len(num.columns) < 2:\n", " return [], []\n", " corr = num.corr()\n", " pr = corr.where(~np.eye(len(corr), dtype=bool)).stack().sort_values(\n", " key=abs, ascending=False)\n", " seen, dup, real = set(), [], []\n", " for (a, b), v in pr.items():\n", " if (b, a) in seen:\n", " continue\n", " seen.add((a, b))\n", " row = {\"pair\": f\"{a} ~ {b}\", \"r\": round(v, 3)}\n", " (dup if abs(v) >= REDUNDANT_R else real).append(row)\n", " return real, dup\n", "\n", "\n", "def make_tables(df: pd.DataFrame, max_card: int = 6) -> dict:\n", " \"\"\"Every table rendered by pandas. The model never transcribes one.\n", "\n", " disable_numparse stops tabulate re-parsing our formatted strings and\n", " re-rendering large values in scientific notation.\n", " \"\"\"\n", " t = {}\n", "\n", " n = df.isna().sum(); n = n[n > 0].sort_values(ascending=False)\n", " if len(n):\n", " head = n.head(15)\n", " tbl = pd.DataFrame({\"missing\": head.map(fmt_num),\n", " \"pct\": (100 * head / len(df)).round(1)}\n", " ).to_markdown(disable_numparse=True)\n", " if len(n) > 15:\n", " tbl += f\"\\n\\n_+{len(n) - 15} more columns with missing values._\"\n", " t[\"## Missing Values\"] = tbl\n", " else:\n", " t[\"## Missing Values\"] = \"_No missing values._\"\n", "\n", " bins = [c for c in df.select_dtypes(include=\"number\") if df[c].nunique() == 2]\n", " blocks = [f\"**mean `{b}` by `{c}`**\\n\\n\"\n", " + df.groupby(c)[b].mean().round(3).to_frame().to_markdown()\n", " for c in df.columns if df[c].nunique(dropna=True) <= max_card\n", " for b in bins if b != c]\n", " if blocks:\n", " t[\"## Group Differences\"] = \"\\n\\n\".join(blocks[:MAX_GROUP_BLOCKS])\n", " if len(blocks) > MAX_GROUP_BLOCKS:\n", " t[\"## Group Differences\"] += (\n", " f\"\\n\\n_+{len(blocks) - MAX_GROUP_BLOCKS} more group comparisons omitted._\")\n", " else:\n", " t[\"## Group Differences\"] = \"_No low-cardinality groupings._\"\n", "\n", " real, dup = corr_pairs(df)\n", " t[\"## Correlations\"] = (pd.DataFrame(real[:8]).to_markdown(index=False) if real\n", " else \"_Too few numeric columns._\")\n", " if dup:\n", " t[\"## Redundant Columns\"] = (\n", " f\"_{len(dup)} column pairs correlate at |r| >= {REDUNDANT_R} — likely duplicate \"\n", " \"encodings rather than findings._\\n\\n\"\n", " + pd.DataFrame(dup[:8]).to_markdown(index=False))\n", "\n", " num = df.select_dtypes(include=\"number\")\n", " if len(num.columns):\n", " desc = num.describe().T\n", " desc = desc.map(fmt_num) if hasattr(desc, \"map\") else desc.applymap(fmt_num)\n", " t[\"## Numeric Summary\"] = desc.to_markdown(disable_numparse=True)\n", " else:\n", " t[\"## Numeric Summary\"] = \"_No numeric columns._\"\n", " return t\n", "\n", "\n", "def key_facts(df: pd.DataFrame, max_card: int = 6) -> dict:\n", " \"\"\"Every superlative computed with idxmax, never looked up by the model.\"\"\"\n", " f = {}\n", "\n", " n = df.isna().sum(); n = n[n > 0]\n", " if len(n):\n", " c = n.idxmax()\n", " f[\"missing\"] = (f\"{c} has the most missing values: \"\n", " f\"{fmt_num(n.max())} ({100*n.max()/len(df):.1f}%)\")\n", "\n", " bins = [c for c in df.select_dtypes(include=\"number\") if df[c].nunique() == 2]\n", " best = None\n", " for c in [c for c in df.columns if df[c].nunique(dropna=True) <= max_card]:\n", " for b in bins:\n", " if b == c:\n", " continue\n", " m = df.groupby(c)[b].mean()\n", " if best is None or m.max() > best[3]:\n", " best = (b, c, m.idxmax(), m.max())\n", " if best:\n", " b, c, k, v = best\n", " f[\"group\"] = f\"the highest mean {b} is {v:.3f}, for {c} = {k}\"\n", "\n", " real, _ = corr_pairs(df)\n", " if real:\n", " r = real[0]\n", " f[\"corr\"] = (f\"the strongest correlation is {r['pair']} at r = {r['r']:+.3f} \"\n", " f\"({'positive' if r['r'] > 0 else 'negative'})\")\n", " return f\n", "\n", "\n", "def first_sentence(text: str) -> str:\n", " \"\"\"First real sentence: skip heading-like fragments, stop at the first period.\"\"\"\n", " lines = [l.strip().lstrip(\"-*# \").strip() for l in text.splitlines() if l.strip()]\n", " lines = [l for l in lines if len(l.split()) >= 4] or lines\n", " s = lines[0] if lines else \"\"\n", " m = re.match(r\"(.+?[.!?])(\\s|$)\", s)\n", " return m.group(1) if m else s[:220]\n", "\n", "\n", "def one_liner(fact: str) -> str:\n", " sys_p = \"Rewrite the given fact as one short sentence. Add nothing. No lists, no headings.\"\n", " return first_sentence(generate(sys_p, fact, max_new_tokens=90))\n", "\n", "\n", "def audit_numbers(md: str, allowed_text: str) -> list:\n", " \"\"\"Flag numerals in the prose absent from the evidence.\n", " Detects fabrication, not misinterpretation. A flag list, not a verdict.\"\"\"\n", " allowed = set(re.findall(r\"\\d+\\.?\\d*\", allowed_text.replace(\",\", \"\")))\n", " prose = \"\\n\".join(l for l in md.splitlines() if not l.strip().startswith(\"|\"))\n", " return sorted({x for x in re.findall(r\"\\d+\\.?\\d*\", prose.replace(\",\", \"\"))\n", " if x not in allowed})\n", "\n", "\n", "def build_report(df, res, name, instruction, dropped=None) -> tuple:\n", " t, kf = make_tables(df), key_facts(df)\n", "\n", " P = [f\"# EDA Report — {name}\", \"\", f\"*{instruction}*\", \"\",\n", " \"## Overview\", one_liner(schema_only(df, name)), \"\"]\n", " if dropped:\n", " P += [f\"_Index-like columns excluded from analysis: {', '.join(dropped)}._\", \"\"]\n", "\n", " P += [\"## Missing Values\", t[\"## Missing Values\"], \"\"]\n", " if \"missing\" in kf:\n", " P += [one_liner(kf[\"missing\"]), \"\"]\n", "\n", " P += [\"## Group Differences\", t[\"## Group Differences\"], \"\"]\n", " if \"group\" in kf:\n", " P += [one_liner(kf[\"group\"]), \"\"]\n", "\n", " P += [\"## Correlations\", t[\"## Correlations\"], \"\"]\n", " if \"corr\" in kf:\n", " P += [one_liner(kf[\"corr\"]), \"\"]\n", "\n", " if \"## Redundant Columns\" in t:\n", " P += [\"## Redundant Columns\", t[\"## Redundant Columns\"], \"\"]\n", "\n", " P += [\"## Numeric Summary\", t[\"## Numeric Summary\"], \"\"]\n", "\n", " take = generate(\"Rewrite each fact as one markdown bullet. Add nothing.\",\n", " \"\\n\".join(f\"- {v}\" for v in kf.values()), max_new_tokens=200)\n", " P += [\"## Takeaways\",\n", " *[l.strip() for l in take.splitlines() if l.strip().startswith((\"-\", \"*\"))][:3], \"\"]\n", "\n", " P += [\"## Plots\", \"\"]\n", " for p in res[\"plots\"]:\n", " P += [f\"**{os.path.basename(p)[:-4].replace('_',' ')}**\", \"\", f\"![]({p})\", \"\"]\n", "\n", " report = \"\\n\".join(P)\n", " allowed = \"\\n\".join([*t.values(), *kf.values(), schema_only(df, name)])\n", " return report, audit_numbers(report, allowed)" ], "metadata": { "id": "PPG4IXipsaBE" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "vZPsfTe3sZ-3" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "VmcPNMn5H2zR" }, "execution_count": 24, "outputs": [] }, { "cell_type": "markdown", "source": [ "the agent" ], "metadata": { "id": "4cEHorxuH-no" } }, { "cell_type": "code", "source": [ "import base64\n", "from IPython.display import Markdown, display\n", "\n", "\n", "def drop_index_cols(df: pd.DataFrame):\n", " \"\"\"Remove monotonic unique integer columns — row ids, not variables.\"\"\"\n", " drop = [c for c in df.columns\n", " if pd.api.types.is_integer_dtype(df[c])\n", " and df[c].is_monotonic_increasing\n", " and df[c].nunique() == len(df)]\n", " return df.drop(columns=drop), drop\n", "\n", "\n", "def embed_images(md: str) -> str:\n", " \"\"\"Inline PNGs as data URIs so they render inside Colab's output iframe.\"\"\"\n", " def repl(m):\n", " path = m.group(1)\n", " if not os.path.exists(path):\n", " return m.group(0)\n", " return (\"](data:image/png;base64,\"\n", " + base64.b64encode(open(path, \"rb\").read()).decode() + \")\")\n", " return re.sub(r\"\\]\\(([^)]+\\.png)\\)\", repl, md)\n", "\n", "\n", "def report_to_pdf(report_md: str, plots: list, path: str = \"eda_report.pdf\") -> str:\n", " \"\"\"Render the markdown report plus every figure to a single PDF.\"\"\"\n", " from fpdf import FPDF\n", " from fpdf.enums import XPos, YPos\n", "\n", " def clean(s):\n", " for a, b in [(\"—\", \"-\"), (\"…\", \"...\"), (\"≥\", \">=\"), (\"×\", \"x\"),\n", " (\"’\", \"'\"), (\"“\", '\"'), (\"”\", '\"')]:\n", " s = s.replace(a, b)\n", " return s.encode(\"latin-1\", \"replace\").decode(\"latin-1\")\n", "\n", " pdf = FPDF(format=\"A4\")\n", " pdf.set_auto_page_break(True, margin=15)\n", " pdf.add_page()\n", "\n", " usable = pdf.w - pdf.l_margin - pdf.r_margin\n", " max_table_chars = int(usable / (6.5 * 0.6 * 0.3528))\n", "\n", " def write(text, style=\"\", size=10, h=5):\n", " \"\"\"Always start at the left margin — otherwise multi_cell width goes to zero.\"\"\"\n", " pdf.set_font(\"Courier\" if style == \"mono\" else \"Helvetica\",\n", " \"B\" if style == \"B\" else \"\", size)\n", " pdf.set_x(pdf.l_margin)\n", " pdf.multi_cell(0, h, text, new_x=XPos.LMARGIN, new_y=YPos.NEXT)\n", "\n", " for raw in report_md.splitlines():\n", " line = clean(raw.rstrip())\n", " if line.startswith(\"![\"):\n", " continue\n", " is_table = line.startswith(\"|\")\n", " if not is_table:\n", " line = re.sub(r\"[*_`]\", \"\", line)\n", "\n", " line = \" \".join(w if len(w) <= 50 else\n", " \" \".join(w[i:i + 50] for i in range(0, len(w), 50))\n", " for w in line.split())\n", " else:\n", " if len(line) > max_table_chars:\n", " line = line[:max_table_chars - 3] + \"...\"\n", "\n", " if line.startswith(\"# \"):\n", " write(line[2:], \"B\", 15, 8); pdf.ln(1)\n", " elif line.startswith(\"## \"):\n", " pdf.ln(2); write(line[3:], \"B\", 12, 7)\n", " elif is_table:\n", " write(line, \"mono\", 6.5, 3.4)\n", " elif line.strip():\n", " write(line, \"\", 10, 5)\n", " else:\n", " pdf.ln(2)\n", "\n", " for p in plots:\n", " pdf.add_page()\n", " write(clean(os.path.basename(p)[:-4].replace(\"_\", \" \")), \"B\", 11, 7)\n", " pdf.image(p, w=usable)\n", "\n", " pdf.output(path)\n", " return path\n", "\n", "\n", "def run_eda_agent(prompt_instruction: str, hf_dataset: str, retries: int = 1) -> dict:\n", " df = normalize(load_hf_dataframe(hf_dataset))\n", " df, dropped = drop_index_cols(df)\n", " if dropped:\n", " print(f\"[agent] dropped index-like columns: {dropped}\")\n", "\n", " context = build_context(df, hf_dataset)\n", " res = generate_and_run(context, prompt_instruction, df, retries=retries)\n", " report, flags = build_report(df, res, hf_dataset, prompt_instruction, dropped)\n", "\n", " safe = re.sub(r\"[^\\w.-]\", \"_\", hf_dataset)\n", " try:\n", " pdf = report_to_pdf(report, res[\"plots\"], path=f\"eda_report_{safe}.pdf\")\n", " except Exception as e:\n", " print(f\"[agent] PDF generation failed: {type(e).__name__}: {e}\")\n", " pdf = None\n", "\n", " return {\"df\": df, \"result\": res, \"report\": report, \"flags\": flags, \"pdf\": pdf}\n", "\n", "\n", "\n", "prompt_instruction = (\"Run a comprehensive exploratory data analysis, highlighting \"\n", " \"missing values, distributions, and key feature correlations.\")\n", "hf_dataset = \"mstz/titanic\"\n", "\n", "out = run_eda_agent(prompt_instruction, hf_dataset)\n", "with open(\"eda_report.md\", \"w\") as f:\n", " f.write(out[\"report\"])\n", "\n", "print(f\"code ok : {out['result']['ok']}\")\n", "print(f\"plots : {len(out['result']['plots'])}\")\n", "print(f\"flags : {out['flags'] or 'none'}\")\n", "print(f\"pdf : {out['pdf']}\")\n", "display(Markdown(embed_images(out[\"report\"])))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "CZ-C3jr_sZ0c", "outputId": "f3770a3d-dbc5-4337-d8a7-c2d1f64d806f" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[loader] load_dataset failed -> RuntimeError: Dataset scripts are no longer supported, but found titanic.py\n", "[loader] fallback -> downloading 'titanic.csv'\n", "[loader] fallback OK\n", "[attempt 1] ok=True\n", "code ok : True\n", "plots : 4\n", "flags : none\n", "pdf : eda_report_mstz_titanic.pdf\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "# EDA Report — mstz/titanic\n\n*Run a comprehensive exploratory data analysis, highlighting missing values, distributions, and key feature correlations.*\n\n## Overview\nThe Titanic dataset contains 891 rows and 12 columns with various features including survival status, passenger class, surname, name, gender, age, number of siblings/spouses aboard, number of parents/children aboard, ticket information, fare paid, cabin location, and embarkation port.\n\n## Missing Values\n| | missing | pct |\n|:---------|:----------|:------|\n| cabin | 687 | 77.1 |\n| age | 177 | 19.9 |\n| embarked | 2 | 0.2 |\n\nThe cabin has the highest percentage of missing values at 77.1%.\n\n## Group Differences\n**mean `has_survived` by `passenger_class`**\n\n| passenger_class | has_survived |\n|------------------:|---------------:|\n| 1 | 0.63 |\n| 2 | 0.473 |\n| 3 | 0.242 |\n\n**mean `has_survived` by `sex`**\n\n| sex | has_survived |\n|:-------|---------------:|\n| female | 0.742 |\n| male | 0.189 |\n\n**mean `has_survived` by `embarked`**\n\n| embarked | has_survived |\n|:-----------|---------------:|\n| C | 0.554 |\n| Q | 0.39 |\n| S | 0.337 |\n\nThe highest mean survival rate among females is 0.742.\n\n## Correlations\n| pair | r |\n|:-------------------------------|-------:|\n| passenger_class ~ fare | -0.549 |\n| sibsp ~ parch | 0.415 |\n| age ~ passenger_class | -0.369 |\n| passenger_class ~ has_survived | -0.338 |\n| sibsp ~ age | -0.308 |\n| has_survived ~ fare | 0.257 |\n| parch ~ fare | 0.216 |\n| age ~ parch | -0.189 |\n\nPassenger class has a negative correlation with fare (-0.549).\n\n## Numeric Summary\n| | count | mean | std | min | 25% | 50% | 75% | max |\n|:----------------|:--------|:-------|:------|:------|:------|:------|:------|:-------|\n| has_survived | 891 | 0.38 | 0.49 | 0 | 0 | 0 | 1 | 1 |\n| passenger_class | 891 | 2.31 | 0.84 | 1 | 2 | 3 | 3 | 3 |\n| age | 714 | 29.70 | 14.53 | 0.42 | 20.12 | 28 | 38 | 80 |\n| sibsp | 891 | 0.52 | 1.10 | 0 | 0 | 0 | 1 | 8 |\n| parch | 891 | 0.38 | 0.81 | 0 | 0 | 0 | 0 | 6 |\n| fare | 891 | 32.20 | 49.69 | 0 | 7.91 | 14.45 | 31 | 512.33 |\n\n## Takeaways\n- cabin has the most missing values: 687 (77.1%)\n- the highest mean has_survived is 0.742, for sex = female\n- the strongest correlation is passenger_class ~ fare at r = -0.549 (negative)\n\n## Plots\n\n**Age Distribution**\n\n![](data:image/png;base64,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)\n\n**Correlation Matrix**\n\n![](data:image/png;base64,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)\n\n**Fare Distribution**\n\n![](data:image/png;base64,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)\n\n**Feature Correlation with Survival**\n\n![](data:image/png;base64,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)\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "47jqehc5voKX" }, "execution_count": 25, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "EDHcw_W3voBU" }, "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "source": [ "One more data set check" ], "metadata": { "id": "1Lusn4nRvofv" } }, { "cell_type": "code", "source": [ "out2 = run_eda_agent(\"Explore this dataset: missing values, distributions, correlations.\",\n", " \"scikit-learn/iris\")\n", "print(f\"code ok : {out2['result']['ok']}\")\n", "print(f\"flags : {out2['flags'] or 'none'}\")\n", "display(Markdown(embed_images(out2[\"report\"])))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "_eP03bp4wsZY", "outputId": "f3953169-d65d-4c28-9f33-07abc638eb38" }, "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[loader] load_dataset OK split='train'\n", "[agent] dropped index-like columns: ['Id']\n", "[attempt 1] ok=True\n", "code ok : True\n", "flags : none\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/markdown": "# EDA Report — scikit-learn/iris\n\n*Explore this dataset: missing values, distributions, correlations.*\n\n## Overview\nThe Iris dataset contains 150 observations with 5 features and a categorical target variable.\n\n_Index-like columns excluded from analysis: Id._\n\n## Missing Values\n_No missing values._\n\n## Group Differences\n_No low-cardinality groupings._\n\n## Correlations\n| pair | r |\n|:------------------------------|-------:|\n| PetalLengthCm ~ PetalWidthCm | 0.963 |\n| SepalLengthCm ~ PetalLengthCm | 0.872 |\n| SepalLengthCm ~ PetalWidthCm | 0.818 |\n| SepalWidthCm ~ PetalLengthCm | -0.421 |\n| SepalWidthCm ~ PetalWidthCm | -0.357 |\n| SepalWidthCm ~ SepalLengthCm | -0.109 |\n\nThe strongest positive correlation between Petal Length and Petal Width is +0.963.\n\n## Numeric Summary\n| | count | mean | std | min | 25% | 50% | 75% | max |\n|:--------------|:--------|:-------|:------|:------|:------|:------|:------|:------|\n| SepalLengthCm | 150 | 5.84 | 0.83 | 4.30 | 5.10 | 5.80 | 6.40 | 7.90 |\n| SepalWidthCm | 150 | 3.05 | 0.43 | 2 | 2.80 | 3 | 3.30 | 4.40 |\n| PetalLengthCm | 150 | 3.76 | 1.76 | 1 | 1.60 | 4.35 | 5.10 | 6.90 |\n| PetalWidthCm | 150 | 1.20 | 0.76 | 0.10 | 0.30 | 1.30 | 1.80 | 2.50 |\n\n## Takeaways\n- The strongest correlation is PetalLengthCm ~ PetalWidthCm with a correlation coefficient of \\( r = 0.963 \\), indicating a positive relationship.\n\n## Plots\n\n**correlation matrix**\n\n![](data:image/png;base64,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)\n\n**pairplot**\n\n![](data:image/png;base64,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)\n\n**petal length distribution**\n\n![](data:image/png;base64,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)\n\n**petal 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distribution**\n\n![](data:image/png;base64,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distribution**\n\n![](data:image/png;base64,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)\n\n**sepal 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distribution**\n\n![](data:image/png;base64,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)\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "r = out2[\"result\"]\n", "if r[\"error\"]:\n", " print(format_error(r[\"error\"], r[\"code\"]))\n", " print(\"\\n--- code that failed ---\\n\")\n", " print(r[\"code\"])\n", "else:\n", " print(\"no error\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KrPAK-nmzMPT", "outputId": "9ee6cb65-1d49-4f72-a4f5-fd5863b906a1" }, "execution_count": 27, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "no error\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "YAxZRUTrF6Nl" }, "execution_count": 27, "outputs": [] }, { "cell_type": "markdown", "source": [ "Quick starters download" ], "metadata": { "id": "EXeP7WZcIWlw" } }, { "cell_type": "code", "source": [ "import json, os, shutil\n", "\n", "QUICKSTARTS = [\n", " {\"slug\": \"titanic\",\n", " \"label\": \"🚢 Titanic — hidden missing values\",\n", " \"dataset\": \"mstz/titanic\",\n", " \"instruction\": (\"Run a comprehensive exploratory data analysis, highlighting \"\n", " \"missing values, distributions, and key feature correlations.\")},\n", " {\"slug\": \"stockmatch\",\n", " \"label\": \"📈 StockMatch — synthetic stocks\",\n", " \"dataset\": \"Kogann/stockmatch-synthetic\",\n", " \"instruction\": \"Explore this dataset: missing values, distributions, correlations.\"},\n", " {\"slug\": \"housing\",\n", " \"label\": \"🏠 Canada housing — skewed prices\",\n", " \"dataset\": \"imanmalhi/canada_realestate_listings\",\n", " \"instruction\": (\"Run a comprehensive exploratory data analysis, highlighting \"\n", " \"missing values, distributions, and key feature correlations.\")},\n", "]\n", "\n", "\n", "def build_quickstarts(specs=QUICKSTARTS, out_dir=\"quickstarts\"):\n", " shutil.rmtree(out_dir, ignore_errors=True)\n", " for s in specs:\n", " d = os.path.join(out_dir, s[\"slug\"])\n", " pdir = os.path.join(d, \"plots\")\n", " os.makedirs(pdir, exist_ok=True)\n", " print(f\"\\n=== {s['slug']} : {s['dataset']} ===\")\n", "\n", " out = run_eda_agent(s[\"instruction\"], s[\"dataset\"])\n", " res = out[\"result\"]\n", "\n", "\n", " report = out[\"report\"]\n", " cached_plots = []\n", " for p in res[\"plots\"]:\n", " dst = os.path.join(pdir, os.path.basename(p))\n", " shutil.copy(p, dst)\n", " cached_plots.append(dst)\n", " report = report.replace(f\"]({p})\", f\"]({dst})\")\n", "\n", " with open(os.path.join(d, \"report.md\"), \"w\") as f:\n", " f.write(report)\n", " with open(os.path.join(d, \"code.py\"), \"w\") as f:\n", " f.write(res.get(\"code\", \"\"))\n", " with open(os.path.join(d, \"stdout.txt\"), \"w\") as f:\n", " f.write(res.get(\"stdout\", \"\"))\n", "\n", " try:\n", " report_to_pdf(report, cached_plots, path=os.path.join(d, \"report.pdf\"))\n", " except Exception as e:\n", " print(f\" [warn] PDF failed: {type(e).__name__}: {e}\")\n", "\n", " json.dump({\"label\": s[\"label\"], \"dataset\": s[\"dataset\"],\n", " \"instruction\": s[\"instruction\"], \"ok\": res[\"ok\"],\n", " \"flags\": out[\"flags\"], \"n_plots\": len(cached_plots)},\n", " open(os.path.join(d, \"meta.json\"), \"w\"), indent=2)\n", "\n", " print(f\" ok={res['ok']} plots={len(cached_plots)} flags={out['flags'] or 'none'}\")\n", "\n", " shutil.make_archive(\"quickstarts\", \"zip\", \".\", out_dir)\n", " size = os.path.getsize(\"quickstarts.zip\") / 1e6\n", " print(f\"\\nWrote quickstarts.zip ({size:.1f} MB)\")\n", " return out_dir\n", "\n", "\n", "build_quickstarts()\n", "\n", "# from google.colab import files\n", "# files.download(\"quickstarts.zip\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 443 }, "id": "5WXZTt-rF59U", "outputId": "4e5e3b8c-3d0b-42bf-8bcb-988aa83e3f88" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=== titanic : mstz/titanic ===\n", "[loader] load_dataset failed -> RuntimeError: Dataset scripts are no longer supported, but found titanic.py\n", "[loader] fallback -> downloading 'titanic.csv'\n", "[loader] fallback OK\n", "[attempt 1] ok=True\n", "[attempt 1] ok=True\n", "[attempt 1] ok=True\n", "[attempt 2] ok=True\n", " ok=True plots=12 flags=['10']\n", "\n", "=== stockmatch : Kogann/stockmatch-synthetic ===\n", "[loader] load_dataset OK split='train'\n", "[agent] dropped index-like columns: ['stock_id']\n", "[attempt 1] ok=True\n", " ok=True plots=7 flags=none\n", "\n", "=== housing : imanmalhi/canada_realestate_listings ===\n", "[loader] load_dataset OK split='train'\n", "[attempt 1] ok=False\n", "[attempt 2] ok=True\n", " ok=True plots=3 flags=none\n", "\n", "Wrote quickstarts.zip (1.4 MB)\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'quickstarts'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "Cubs83ZEIn4F" }, "execution_count": 27, "outputs": [] }, { "cell_type": "markdown", "source": [ "Gradio" ], "metadata": { "id": "VKrLZTErF6dL" } }, { "cell_type": "code", "source": [ "!pip install -q gradio\n", "import json\n", "import gradio as gr\n", "from IPython.display import HTML, display\n", "\n", "\n", "_required = [\"load_hf_dataframe\", \"normalize\", \"drop_index_cols\", \"build_context\",\n", " \"generate_and_run\", \"build_report\", \"embed_images\", \"report_to_pdf\",\n", " \"generate\", \"model\"]\n", "_missing = [n for n in _required if n not in globals()]\n", "if _missing:\n", " raise NameError(f\"Run cells 0-10 first (Runtime > Run all). Missing: {_missing}\")\n", "\n", "DEFAULT_INSTRUCTION = (\"Run a comprehensive exploratory data analysis, highlighting \"\n", " \"missing values, distributions, and key feature correlations.\")\n", "DEFAULT_DATASET = \"Kogann/stockmatch-synthetic\"\n", "\n", "QS_DIR = \"quickstarts\"\n", "QUICKSTARTS = [\n", " {\"slug\": \"titanic\",\n", " \"label\": \"🚢 Titanic — hidden missing values\",\n", " \"blurb\": \"77% of `cabin` is missing, but the raw file hides it as `''`.\",\n", " \"dataset\": \"mstz/titanic\",\n", " \"instruction\": DEFAULT_INSTRUCTION},\n", " {\"slug\": \"stockmatch\",\n", " \"label\": \"📈 StockMatch — synthetic stocks\",\n", " \"blurb\": \"12,500 generated stocks — my own synthetic, stock data set from the final project\",\n", " \"dataset\": \"Kogann/stockmatch-synthetic\",\n", " \"instruction\": \"Explore this dataset: missing values, distributions, correlations.\"},\n", " {\"slug\": \"housing\",\n", " \"label\": \"🏠 Canada housing — skewed prices\",\n", " \"blurb\": \"35,768 listings with a long price tail the agent replots on a log axis.\",\n", " \"dataset\": \"imanmalhi/canada_realestate_listings\",\n", " \"instruction\": DEFAULT_INSTRUCTION},\n", "]\n", "\n", "_CACHE = {}\n", "BLANK = (None, \"\", \"\", [], \"\", \"\")\n", "\n", "\n", "def gradio_agent(instruction, dataset):\n", " \"\"\"Streams: status, pdf, code, stdout, gallery, report, flags.\"\"\"\n", " instruction, dataset = (instruction or \"\").strip(), (dataset or \"\").strip()\n", "\n", " key = (instruction, dataset)\n", " if key in _CACHE:\n", " status, *rest = _CACHE[key]\n", " yield (status + \" _(cached — nothing recomputed)_\", *rest)\n", " return\n", "\n", " yield (f\"Loading `{dataset}` …\", *BLANK)\n", " try:\n", " df = normalize(load_hf_dataframe(dataset))\n", " except Exception as e:\n", " yield (f\"**Could not load `{dataset}`** — {type(e).__name__}: {e}\", *BLANK)\n", " return\n", "\n", " df, dropped = drop_index_cols(df)\n", " context = build_context(df, dataset)\n", " note = f\" Dropped index-like columns: {', '.join(dropped)}.\" if dropped else \"\"\n", " yield (f\"Loaded **{df.shape[0]} x {df.shape[1]}**.{note} Generating analysis code …\",\n", " *BLANK)\n", "\n", " res = generate_and_run(context, instruction, df)\n", " status = (\"Code ran successfully\" if res[\"ok\"]\n", " else \"Code still failing after one repair — report built from data only\")\n", " yield (f\"{status}. Writing report …\",\n", " None, res[\"code\"], res[\"stdout\"], res[\"plots\"], \"\", \"\")\n", "\n", " report, flags = build_report(df, res, dataset, instruction, dropped)\n", " safe = re.sub(r\"[^\\w.-]\", \"_\", dataset)\n", " try:\n", " pdf = report_to_pdf(report, res[\"plots\"], path=f\"eda_report_{safe}.pdf\")\n", " pdf_note = \"\"\n", " except Exception as e:\n", " pdf, pdf_note = None, f\" (PDF unavailable: {type(e).__name__})\"\n", "\n", " final = (f\"Done — {status.lower()}, {len(res['plots'])} plots.{pdf_note}\",\n", " pdf, res[\"code\"], res[\"stdout\"], res[\"plots\"],\n", " embed_images(report), \", \".join(flags) if flags else \"none\")\n", " _CACHE[key] = final\n", " yield final\n", "\n", "\n", "def load_quickstart(spec):\n", " \"\"\"Read a pre-generated run from disk (built by Cell 12). None if absent.\"\"\"\n", " d = os.path.join(QS_DIR, spec[\"slug\"])\n", " meta_path = os.path.join(d, \"meta.json\")\n", " if not os.path.exists(meta_path):\n", " return None\n", " meta = json.load(open(meta_path))\n", " report = open(os.path.join(d, \"report.md\")).read()\n", " code = open(os.path.join(d, \"code.py\")).read()\n", " stdout = open(os.path.join(d, \"stdout.txt\")).read()\n", " plots = sorted(glob.glob(os.path.join(d, \"plots\", \"*.png\")))\n", " pdf = os.path.join(d, \"report.pdf\")\n", " return (spec[\"instruction\"], spec[\"dataset\"],\n", " f\"**{spec['label']}** — pre-generated example, loaded instantly.\",\n", " pdf if os.path.exists(pdf) else None,\n", " code, stdout, plots, embed_images(report),\n", " \", \".join(meta.get(\"flags\") or []) or \"none\")\n", "\n", "\n", "def quickstart_handler(spec):\n", " \"\"\"Serve from the cache dir; fall back to a live run if it wasn't built yet.\"\"\"\n", " def handler():\n", " cached = load_quickstart(spec)\n", " if cached is not None:\n", " return cached\n", " last = None\n", " for out in gradio_agent(spec[\"instruction\"], spec[\"dataset\"]):\n", " last = out\n", " status, pdf, code, stdout, plots, report, flags = last\n", " return (spec[\"instruction\"], spec[\"dataset\"], status, pdf,\n", " code, stdout, plots, report, flags)\n", " return handler\n", "\n", "\n", "def reset_form():\n", " return DEFAULT_INSTRUCTION, DEFAULT_DATASET\n", "\n", "\n", "\n", "CSS = \"\"\"\n", ".hero {text-align:center;}\n", ".qs-card {border:1px solid var(--border-color-primary);\n", " border-radius:12px; padding:10px 12px;}\n", "\"\"\"\n", "\n", "with gr.Blocks(title=\"EDA Agent\", theme=gr.themes.Soft(), css=CSS) as demo:\n", " gr.Markdown(\n", " \"# 📊 EDAgent\\n\"\n", " \"**Point it at any Hugging Face dataset. It writes its own analysis code, \"\n", " \"runs it, fixes it when it crashes, and hands back a report.**\",\n", " elem_classes=\"hero\",\n", " )\n", " gr.Markdown(\n", " \"Powered by `Qwen2.5-Coder-1.5B-Instruct`. The model writes **code and prose \"\n", " \"only** — every table, plot and “which is highest” lookup is computed in \"\n", " \"pandas, so the numbers in the report cannot be hallucinated. \"\n", " \"A built-in check flags any figure in the text that is missing from the data.\"\n", " )\n", "\n", " gr.Markdown(\"### ⚡ Quick Starters — one click\")\n", " with gr.Row():\n", " qs_buttons = []\n", " for spec in QUICKSTARTS:\n", " with gr.Column(elem_classes=\"qs-card\"):\n", " btn = gr.Button(spec[\"label\"], variant=\"secondary\", size=\"lg\")\n", " gr.Markdown(f\"{spec['blurb']}\")\n", " qs_buttons.append((btn, spec))\n", "\n", " gr.Markdown(\"### 🔎 Or run it on any dataset\")\n", " with gr.Row():\n", " instruction = gr.Textbox(label=\"Prompt instruction\", value=DEFAULT_INSTRUCTION,\n", " lines=3, scale=3)\n", " dataset = gr.Textbox(label=\"Hugging Face dataset id\", value=DEFAULT_DATASET,\n", " placeholder=\"owner/name\", lines=1, scale=1)\n", " with gr.Row():\n", " run_btn = gr.Button(\"🚀 Run EDA Agent\", variant=\"primary\", size=\"lg\", scale=4)\n", " reset_btn = gr.Button(\"↺ Reset\", variant=\"secondary\", size=\"lg\", scale=1)\n", "\n", " gr.Markdown(\"A fresh run takes about a minute on the T4. Repeat runs of the \"\n", " \"same dataset are served from memory.\")\n", "\n", " status = gr.Markdown()\n", " pdf_file = gr.File(label=\"⬇️ Full report as PDF (text, tables and every plot)\")\n", "\n", " with gr.Tabs():\n", " with gr.Tab(\"📄 Report\"):\n", " report_md = gr.Markdown()\n", " flags_box = gr.Textbox(label=\"Unverified numbers (fabrication check)\",\n", " interactive=False)\n", " with gr.Tab(\"🖼️ Plots\"):\n", " gallery = gr.Gallery(label=\"Plots\", columns=2, height=560)\n", " with gr.Tab(\"🐍 Generated code\"):\n", " code_box = gr.Code(language=\"python\", label=\"Model-written analysis code\")\n", " with gr.Tab(\"🖥️ Execution output\"):\n", " stdout_box = gr.Textbox(label=\"stdout / traceback\", lines=18,\n", " interactive=False)\n", "\n", " live_outputs = [status, pdf_file, code_box, stdout_box, gallery, report_md, flags_box]\n", " qs_outputs = [instruction, dataset] + live_outputs\n", "\n", " run_btn.click(gradio_agent, [instruction, dataset], live_outputs)\n", " reset_btn.click(reset_form, None, [instruction, dataset])\n", " for btn, spec in qs_buttons:\n", " btn.click(quickstart_handler(spec), None, qs_outputs)\n", "\n", "\n", "\n", "app, local_url, share_url = demo.queue().launch(\n", " share=True, debug=False, quiet=True\n", ")\n", "\n", "display(HTML(f\"\"\"\n", "
\n", "\n", "
\n", " 📊  Your EDA Agent is live\n", "
\n", "\n", " \n", " {share_url}\n", " \n", "\n", "
\n", " Share it with anyone — no account or install needed.
\n", " The link stays open for as long as this Colab runtime is running.\n", "
\n", "\n", "
\n", "\"\"\"))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 770 }, "id": "w2_AdLZsF6w-", "outputId": "c168273a-5f21-47dc-fdbb-9658b1f237c2" }, "execution_count": 28, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "* Running on public URL: https://0dc5e073f467791c6f.gradio.live\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", "\n", "
\n", " 📊  Your EDA Agent is live\n", "
\n", "\n", " \n", " https://0dc5e073f467791c6f.gradio.live\n", " \n", "\n", "
\n", " Share it with anyone — no account or install needed.
\n", " The link stays open for as long as this Colab runtime is running.\n", "
\n", "\n", "
\n" ] }, "metadata": {} } ] } ] }