diff --git a/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/thread.json b/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/thread.json
new file mode 100644
index 0000000..3fdd8ed
--- /dev/null
+++ b/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/thread.json
@@ -0,0 +1,584 @@
+{
+ "values": {
+ "messages": [
+ {
+ "content": [
+ {
+ "type": "text",
+ "text": "What interesting findings can be made from this dataset?"
+ }
+ ],
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "human",
+ "name": null,
+ "id": "ac8f27cd-0f2e-4a82-a432-f4b37d18a846"
+ },
+ {
+ "content": "\nThe following files have been uploaded and are available for use:\n\n- titanic.csv (58.9 KB)\n Path: /mnt/user-data/uploads/titanic.csv\n\nYou can read these files using the `read_file` tool with the paths shown above.\n",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "system",
+ "name": null,
+ "id": "915c3f95-01d0-4308-b284-ddb5edc74728"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "I need to examine the Titanic dataset first to understand its structure and then perform analysis to find interesting findings. Let me start by reading the uploaded CSV file."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0c9-2299-73c3-ab06-a4cbfac799f6",
+ "tool_calls": [
+ {
+ "name": "read_file",
+ "args": {
+ "description": "Read the Titanic dataset to understand its structure",
+ "path": "/mnt/user-data/uploads/titanic.csv"
+ },
+ "id": "call_h10krmevrts6zx0xf41ltctx",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
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+ "tool_call_id": "call_h10krmevrts6zx0xf41ltctx",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "I can see this is the Titanic dataset. I need to analyze it to find interesting findings. Let me first check the full dataset size and then run some analysis using Python. Let me start by listing the workspace and then writing a Python script to analyze the data."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
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+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0c9-3e06-7cf0-a5ac-8a4e3a05107a",
+ "tool_calls": [
+ {
+ "name": "ls",
+ "args": {
+ "description": "Check workspace directory structure",
+ "path": "/mnt/user-data/workspace"
+ },
+ "id": "call_tnspi0a92f5xf9viqr9o9kew",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "/mnt/user-data/workspace",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
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+ "id": "ec49e5f8-29be-4365-b318-a5cf43a0a221",
+ "tool_call_id": "call_tnspi0a92f5xf9viqr9o9kew",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Now I need to analyze the Titanic dataset to find interesting findings. I'll write a Python script to explore the data and generate insights. First, let me create a workspace and analyze the data."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0c9-6741-7952-ad2a-e03471bb6d48",
+ "tool_calls": [
+ {
+ "name": "write_file",
+ "args": {
+ "description": "Create Python script to analyze Titanic dataset",
+ "path": "/mnt/user-data/workspace/analyze_titanic.py",
+ "content": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\n\n# Load the dataset\ndf = pd.read_csv('/mnt/user-data/uploads/titanic.csv')\n\nprint(\"=== TITANIC DATASET ANALYSIS ===\\n\")\nprint(f\"Dataset shape: {df.shape}\")\nprint(f\"Columns: {list(df.columns)}\")\nprint(\"\\nFirst 5 rows:\")\nprint(df.head())\nprint(\"\\nBasic info:\")\nprint(df.info())\n\n# Check for missing values\nprint(\"\\n=== MISSING VALUES ===\")\nmissing = df.isnull().sum()\nmissing_pct = (missing / len(df)) * 100\nmissing_df = pd.DataFrame({'missing_count': missing, 'missing_percentage': missing_pct})\nprint(missing_df[missing_df['missing_count'] > 0])\n\n# Basic statistics\nprint(\"\\n=== BASIC STATISTICS ===\")\nprint(df.describe(include='all'))\n\n# Survival rate overall\nprint(\"\\n=== SURVIVAL ANALYSIS ===\")\nsurvival_rate = df['Survived'].mean() * 100\nprint(f\"Overall survival rate: {survival_rate:.2f}% ({df['Survived'].sum()} out of {len(df)})\")\n\n# Survival by gender\nprint(\"\\n--- Survival by Gender ---\")\ngender_survival = df.groupby('Sex')['Survived'].agg(['mean', 'count', 'sum'])\ngender_survival['mean'] = gender_survival['mean'] * 100\nprint(gender_survival)\n\n# Survival by passenger class\nprint(\"\\n--- Survival by Passenger Class ---\")\nclass_survival = df.groupby('Pclass')['Survived'].agg(['mean', 'count', 'sum'])\nclass_survival['mean'] = class_survival['mean'] * 100\nprint(class_survival)\n\n# Survival by age groups\nprint(\"\\n--- Survival by Age Groups ---\")\ndf['AgeGroup'] = pd.cut(df['Age'], bins=[0, 12, 18, 30, 50, 80], \n labels=['Child (0-12)', 'Teen (13-18)', 'Young Adult (19-30)', \n 'Adult (31-50)', 'Senior (51+)'])\nage_survival = df.groupby('AgeGroup')['Survived'].agg(['mean', 'count', 'sum'])\nage_survival['mean'] = age_survival['mean'] * 100\nprint(age_survival)\n\n# Survival by embarkation port\nprint(\"\\n--- Survival by Embarkation Port ---\")\nembark_survival = df.groupby('Embarked')['Survived'].agg(['mean', 'count', 'sum'])\nembark_survival['mean'] = embark_survival['mean'] * 100\nprint(embark_survival)\n\n# Family size analysis\nprint(\"\\n--- Survival by Family Size ---\")\ndf['FamilySize'] = df['SibSp'] + df['Parch'] + 1\ndf['IsAlone'] = df['FamilySize'] == 1\nfamily_survival = df.groupby('IsAlone')['Survived'].agg(['mean', 'count', 'sum'])\nfamily_survival['mean'] = family_survival['mean'] * 100\nprint(family_survival)\n\n# Fare analysis\nprint(\"\\n--- Fare Analysis ---\")\nprint(f\"Average fare: ${df['Fare'].mean():.2f}\")\nprint(f\"Median fare: ${df['Fare'].median():.2f}\")\nprint(f\"Maximum fare: ${df['Fare'].max():.2f}\")\nprint(f\"Minimum fare: ${df['Fare'].min():.2f}\")\n\n# Fare by class\nprint(\"\\n--- Fare by Passenger Class ---\")\nfare_by_class = df.groupby('Pclass')['Fare'].agg(['mean', 'median', 'min', 'max'])\nprint(fare_by_class)\n\n# Cabin analysis (only 204 out of 891 have cabin info)\nprint(\"\\n--- Cabin Analysis ---\")\ndf['HasCabin'] = df['Cabin'].notna()\ncabin_survival = df.groupby('HasCabin')['Survived'].agg(['mean', 'count', 'sum'])\ncabin_survival['mean'] = cabin_survival['mean'] * 100\nprint(cabin_survival)\n\n# Interesting combinations\nprint(\"\\n=== INTERESTING COMBINATIONS ===\")\n\n# Women and children first?\nprint(\"\\n--- 'Women and Children First' Hypothesis ---\")\ndf['IsChild'] = df['Age'] < 18\ndf['IsFemale'] = df['Sex'] == 'female'\ndf['IsFemaleOrChild'] = df['IsFemale'] | df['IsChild']\n\ngroup_survival = df.groupby('IsFemaleOrChild')['Survived'].agg(['mean', 'count', 'sum'])\ngroup_survival['mean'] = group_survival['mean'] * 100\nprint(group_survival)\n\n# Class and gender interaction\nprint(\"\\n--- Survival by Class and Gender ---\")\nclass_gender_survival = df.groupby(['Pclass', 'Sex'])['Survived'].agg(['mean', 'count', 'sum'])\nclass_gender_survival['mean'] = class_gender_survival['mean'] * 100\nprint(class_gender_survival)\n\n# Age and class interaction\nprint(\"\\n--- Average Age by Class and Survival ---\")\nage_class_survival = df.groupby(['Pclass', 'Survived'])['Age'].agg(['mean', 'median', 'count'])\nprint(age_class_survival)\n\n# Family survival patterns\nprint(\"\\n--- Family Survival Patterns ---\")\n# Check if having family members increased survival chances\nfamily_pattern = df.groupby('FamilySize')['Survived'].agg(['mean', 'count'])\nfamily_pattern['mean'] = family_pattern['mean'] * 100\nprint(family_pattern.sort_values('mean', ascending=False).head(10))\n\n# Titles from names\nprint(\"\\n--- Title Analysis from Names ---\")\ndf['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\ntitle_counts = df['Title'].value_counts()\nprint(f\"Unique titles: {df['Title'].nunique()}\")\nprint(f\"Most common titles:\\n{title_counts.head(10)}\")\n\n# Survival by title\ntitle_survival = df.groupby('Title')['Survived'].agg(['mean', 'count', 'sum'])\ntitle_survival['mean'] = title_survival['mean'] * 100\nprint(\"\\nSurvival by title (top 10 by count):\")\nprint(title_survival.sort_values('count', ascending=False).head(10))\n\n# Save summary to file\nsummary_path = '/mnt/user-data/workspace/titanic_summary.txt'\nwith open(summary_path, 'w') as f:\n f.write(\"=== TITANIC DATASET ANALYSIS SUMMARY ===\\n\\n\")\n f.write(f\"Dataset shape: {df.shape}\\n\")\n f.write(f\"Total passengers: {len(df)}\\n\")\n f.write(f\"Survivors: {df['Survived'].sum()} ({survival_rate:.2f}%)\\n\\n\")\n \n f.write(\"KEY FINDINGS:\\n\")\n f.write(\"1. Gender disparity: Female survival rate was much higher than male.\\n\")\n f.write(f\" - Female: {gender_survival.loc['female', 'mean']:.2f}%\\n\")\n f.write(f\" - Male: {gender_survival.loc['male', 'mean']:.2f}%\\n\\n\")\n \n f.write(\"2. Class disparity: Higher classes had better survival rates.\\n\")\n for pclass in [1, 2, 3]:\n rate = class_survival.loc[pclass, 'mean']\n count = class_survival.loc[pclass, 'sum']\n total = class_survival.loc[pclass, 'count']\n f.write(f\" - Class {pclass}: {rate:.2f}% ({count}/{total})\\n\")\n \n f.write(\"\\n3. Children had better survival rates than adults.\\n\")\n for age_group in age_survival.index:\n if not pd.isna(age_group):\n rate = age_survival.loc[age_group, 'mean']\n count = age_survival.loc[age_group, 'sum']\n total = age_survival.loc[age_group, 'count']\n f.write(f\" - {age_group}: {rate:.2f}% ({count}/{total})\\n\")\n \n f.write(\"\\n4. Passengers with cabins had much higher survival rates.\\n\")\n f.write(f\" - With cabin: {cabin_survival.loc[True, 'mean']:.2f}%\\n\")\n f.write(f\" - Without cabin: {cabin_survival.loc[False, 'mean']:.2f}%\\n\")\n \n f.write(\"\\n5. Family size affected survival.\\n\")\n f.write(f\" - Alone: {family_survival.loc[True, 'mean']:.2f}%\\n\")\n f.write(f\" - With family: {family_survival.loc[False, 'mean']:.2f}%\\n\")\n \n f.write(\"\\n6. Embarkation port correlated with survival.\\n\")\n for port in ['C', 'Q', 'S']:\n if port in embark_survival.index:\n rate = embark_survival.loc[port, 'mean']\n count = embark_survival.loc[port, 'sum']\n total = embark_survival.loc[port, 'count']\n f.write(f\" - Port {port}: {rate:.2f}% ({count}/{total})\\n\")\n\nprint(f\"\\nSummary saved to: {summary_path}\")"
+ },
+ "id": "call_lg6r7qzq95zz7y54hle3e5ez",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "OK",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "write_file",
+ "id": "287af69b-3c5a-48a5-ac74-3ad20d159753",
+ "tool_call_id": "call_lg6r7qzq95zz7y54hle3e5ez",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Now I need to run the analysis script to generate insights. Let me execute it."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0ca-9d40-7c32-bac0-86acd72cd6fe",
+ "tool_calls": [
+ {
+ "name": "bash",
+ "args": {
+ "description": "Run Titanic dataset analysis",
+ "command": "cd /mnt/user-data/workspace && python analyze_titanic.py"
+ },
+ "id": "call_et2tzk37y8hlljtfbgdc39hv",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "=== TITANIC DATASET ANALYSIS ===\n\nDataset shape: (891, 12)\nColumns: ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n\nFirst 5 rows:\n PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S\n1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C\n2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S\n3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S\n4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S\n\nBasic info:\n\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\n # Column Non-Null Count Dtype\n--- ------ -------------- -----\n 0 PassengerId 891 non-null int64\n 1 Survived 891 non-null int64\n 2 Pclass 891 non-null int64\n 3 Name 891 non-null object\n 4 Sex 891 non-null object\n 5 Age 714 non-null float64\n 6 SibSp 891 non-null int64\n 7 Parch 891 non-null int64\n 8 Ticket 891 non-null object\n 9 Fare 891 non-null float64\n 10 Cabin 204 non-null object\n 11 Embarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\nNone\n\n=== MISSING VALUES ===\n missing_count missing_percentage\nAge 177 19.865320\nCabin 687 77.104377\nEmbarked 2 0.224467\n\n=== BASIC STATISTICS ===\n PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\ncount 891.000000 891.000000 891.000000 891 891 714.000000 891.000000 891.000000 891 891.000000 204 889\nunique NaN NaN NaN 891 2 NaN NaN NaN 681 NaN 147 3\ntop NaN NaN NaN Braund, Mr. Owen Harris male NaN NaN NaN 347082 NaN B96 B98 S\nfreq NaN NaN NaN 1 577 NaN NaN NaN 7 NaN 4 644\nmean 446.000000 0.383838 2.308642 NaN NaN 29.699118 0.523008 0.381594 NaN 32.204208 NaN NaN\nstd 257.353842 0.486592 0.836071 NaN NaN 14.526497 1.102743 0.806057 NaN 49.693429 NaN NaN\nmin 1.000000 0.000000 1.000000 NaN NaN 0.420000 0.000000 0.000000 NaN 0.000000 NaN NaN\n25% 223.500000 0.000000 2.000000 NaN NaN 20.125000 0.000000 0.000000 NaN 7.910400 NaN NaN\n50% 446.000000 0.000000 3.000000 NaN NaN 28.000000 0.000000 0.000000 NaN 14.454200 NaN NaN\n75% 668.500000 1.000000 3.000000 NaN NaN 38.000000 1.000000 0.000000 NaN 31.000000 NaN NaN\nmax 891.000000 1.000000 3.000000 NaN NaN 80.000000 8.000000 6.000000 NaN 512.329200 NaN NaN\n\n=== SURVIVAL ANALYSIS ===\nOverall survival rate: 38.38% (342 out of 891)\n\n--- Survival by Gender ---\n mean count sum\nSex\nfemale 74.203822 314 233\nmale 18.890815 577 109\n\n--- Survival by Passenger Class ---\n mean count sum\nPclass\n1 62.962963 216 136\n2 47.282609 184 87\n3 24.236253 491 119\n\n--- Survival by Age Groups ---\n mean count sum\nAgeGroup\nChild (0-12) 57.971014 69 40\nTeen (13-18) 42.857143 70 30\nYoung Adult (19-30) 35.555556 270 96\nAdult (31-50) 42.323651 241 102\nSenior (51+) 34.375000 64 22\n\n--- Survival by Embarkation Port ---\n mean count sum\nEmbarked\nC 55.357143 168 93\nQ 38.961039 77 30\nS 33.695652 644 217\n\n--- Survival by Family Size ---\n mean count sum\nIsAlone\nFalse 50.564972 354 179\nTrue 30.353818 537 163\n\n--- Fare Analysis ---\nAverage fare: $32.20\nMedian fare: $14.45\nMaximum fare: $512.33\nMinimum fare: $0.00\n\n--- Fare by Passenger Class ---\n mean median min max\nPclass\n1 84.154687 60.2875 0.0 512.3292\n2 20.662183 14.2500 0.0 73.5000\n3 13.675550 8.0500 0.0 69.5500\n\n--- Cabin Analysis ---\n mean count sum\nHasCabin\nFalse 29.985444 687 206\nTrue 66.666667 204 136\n\n=== INTERESTING COMBINATIONS ===\n\n--- 'Women and Children First' Hypothesis ---\n mean count sum\nIsFemaleOrChild\nFalse 16.570328 519 86\nTrue 68.817204 372 256\n\n--- Survival by Class and Gender ---\n mean count sum\nPclass Sex\n1 female 96.808511 94 91\n male 36.885246 122 45\n2 female 92.105263 76 70\n male 15.740741 108 17\n3 female 50.000000 144 72\n male 13.544669 347 47\n\n--- Average Age by Class and Survival ---\n mean median count\nPclass Survived\n1 0 43.695312 45.25 64\n 1 35.368197 35.00 122\n2 0 33.544444 30.50 90\n 1 25.901566 28.00 83\n3 0 26.555556 25.00 270\n 1 20.646118 22.00 85\n\n--- Family Survival Patterns ---\n mean count\nFamilySize\n4 72.413793 29\n3 57.843137 102\n2 55.279503 161\n7 33.333333 12\n1 30.353818 537\n5 20.000000 15\n6 13.636364 22\n8 0.000000 6\n11 0.000000 7\n\n--- Title Analysis from Names ---\nUnique titles: 17\nMost common titles:\nTitle\nMr 517\nMiss 182\nMrs 125\nMaster 40\nDr 7\nRev 6\nMlle 2\nMajor 2\nCol 2\nCountess 1\nName: count, dtype: int64\n\nSurvival by title (top 10 by count):\n mean count sum\nTitle\nMr 15.667311 517 81\nMiss 69.780220 182 127\nMrs 79.200000 125 99\nMaster 57.500000 40 23\nDr 42.857143 7 3\nRev 0.000000 6 0\nMajor 50.000000 2 1\nCol 50.000000 2 1\nMlle 100.000000 2 2\nMme 100.000000 1 1\n\nSummary saved to: /mnt/user-data/workspace/titanic_summary.txt",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "bash",
+ "id": "4ff1ccf1-c606-44af-a457-2679d76fc3c4",
+ "tool_call_id": "call_et2tzk37y8hlljtfbgdc39hv",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Now I'll create a visualization script to generate some charts and then present the findings. Let me create visualizations first."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0ca-c59e-7fe2-9e08-8fdff0919575",
+ "tool_calls": [
+ {
+ "name": "write_file",
+ "args": {
+ "description": "Create visualization script for Titanic dataset",
+ "path": "/mnt/user-data/workspace/visualize_titanic.py",
+ "content": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\n\n# Set style\nplt.style.use('seaborn-v0_8-darkgrid')\nsns.set_palette(\"husl\")\n\n# Load the dataset\ndf = pd.read_csv('/mnt/user-data/uploads/titanic.csv')\n\n# Create output directory\noutput_dir = Path('/mnt/user-data/workspace/visualizations')\noutput_dir.mkdir(exist_ok=True)\n\n# Prepare some derived columns\ndf['AgeGroup'] = pd.cut(df['Age'], bins=[0, 12, 18, 30, 50, 80], \n labels=['Child (0-12)', 'Teen (13-18)', 'Young Adult (19-30)', \n 'Adult (31-50)', 'Senior (51+)'])\ndf['FamilySize'] = df['SibSp'] + df['Parch'] + 1\ndf['IsAlone'] = df['FamilySize'] == 1\ndf['HasCabin'] = df['Cabin'].notna()\ndf['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n\n# 1. Overall Survival Pie Chart\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\nsurvival_counts = df['Survived'].value_counts()\ncolors = ['#ff6b6b', '#4ecdc4']\naxes[0].pie(survival_counts, labels=['Perished', 'Survived'], autopct='%1.1f%%', \n colors=colors, startangle=90)\naxes[0].set_title('Overall Survival Rate (n=891)')\n\n# 2. Survival by Gender\ngender_survival = df.groupby('Sex')['Survived'].mean() * 100\nbars = axes[1].bar(gender_survival.index, gender_survival.values, color=['#ff6b6b', '#4ecdc4'])\naxes[1].set_title('Survival Rate by Gender')\naxes[1].set_ylabel('Survival Rate (%)')\naxes[1].set_ylim(0, 100)\nfor bar, value in zip(bars, gender_survival.values):\n axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, \n f'{value:.1f}%', ha='center', va='bottom')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'survival_overview.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 3. Survival by Passenger Class\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Bar chart\nclass_survival = df.groupby('Pclass')['Survived'].mean() * 100\nbars = axes[0].bar([f'Class {c}' for c in class_survival.index], class_survival.values, \n color=['#ffd166', '#06d6a0', '#118ab2'])\naxes[0].set_title('Survival Rate by Passenger Class')\naxes[0].set_ylabel('Survival Rate (%)')\naxes[0].set_ylim(0, 100)\nfor bar, value in zip(bars, class_survival.values):\n axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, \n f'{value:.1f}%', ha='center', va='bottom')\n\n# Stacked bar chart\nclass_survival_counts = pd.crosstab(df['Pclass'], df['Survived'])\nclass_survival_counts.columns = ['Perished', 'Survived']\nclass_survival_counts.plot(kind='bar', stacked=True, ax=axes[1], \n color=['#ff6b6b', '#4ecdc4'])\naxes[1].set_title('Passenger Count by Class and Survival')\naxes[1].set_ylabel('Number of Passengers')\naxes[1].set_xlabel('Passenger Class')\naxes[1].legend(title='Outcome')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'survival_by_class.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 4. Survival by Age Groups\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Age distribution by survival\naxes[0].hist([df[df['Survived'] == 0]['Age'].dropna(), \n df[df['Survived'] == 1]['Age'].dropna()], \n bins=20, stacked=True, label=['Perished', 'Survived'],\n color=['#ff6b6b', '#4ecdc4'], alpha=0.7)\naxes[0].set_title('Age Distribution by Survival Status')\naxes[0].set_xlabel('Age')\naxes[0].set_ylabel('Number of Passengers')\naxes[0].legend()\n\n# Survival rate by age group\nage_survival = df.groupby('AgeGroup')['Survived'].mean() * 100\nbars = axes[1].bar(age_survival.index.astype(str), age_survival.values, \n color='#118ab2')\naxes[1].set_title('Survival Rate by Age Group')\naxes[1].set_ylabel('Survival Rate (%)')\naxes[1].set_ylim(0, 100)\naxes[1].tick_params(axis='x', rotation=45)\nfor bar, value in zip(bars, age_survival.values):\n if not pd.isna(value):\n axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, \n f'{value:.1f}%', ha='center', va='bottom')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'survival_by_age.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 5. Fare vs Survival\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Box plot of fare by survival\nsns.boxplot(x='Survived', y='Fare', data=df, ax=axes[0], \n palette={0: '#ff6b6b', 1: '#4ecdc4'})\naxes[0].set_title('Fare Distribution by Survival')\naxes[0].set_xlabel('Survived (0=No, 1=Yes)')\naxes[0].set_ylabel('Fare ($)')\naxes[0].set_xticklabels(['Perished', 'Survived'])\n\n# Fare by class and survival\nsns.boxplot(x='Pclass', y='Fare', hue='Survived', data=df, ax=axes[1],\n palette={0: '#ff6b6b', 1: '#4ecdc4'})\naxes[1].set_title('Fare by Class and Survival')\naxes[1].set_xlabel('Passenger Class')\naxes[1].set_ylabel('Fare ($)')\naxes[1].legend(title='Survived', labels=['Perished', 'Survived'])\n\nplt.tight_layout()\nplt.savefig(output_dir / 'fare_analysis.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 6. Family Size Analysis\nfig, ax = plt.subplots(figsize=(10, 6))\n\nfamily_survival = df.groupby('FamilySize')['Survived'].mean() * 100\ncounts = df.groupby('FamilySize').size()\n\n# Create scatter plot with size proportional to count\nscatter = ax.scatter(family_survival.index, family_survival.values, \n s=counts * 10, alpha=0.6, color='#118ab2')\nax.set_title('Survival Rate by Family Size (Bubble size = passenger count)')\nax.set_xlabel('Family Size')\nax.set_ylabel('Survival Rate (%)')\nax.set_ylim(0, 100)\nax.grid(True, alpha=0.3)\n\n# Add labels for some points\nfor size in [1, 2, 3, 4]:\n if size in family_survival.index:\n ax.annotate(f'{counts[size]} passengers', \n xy=(size, family_survival[size]),\n xytext=(size, family_survival[size] + 5),\n ha='center', fontsize=9)\n\nplt.tight_layout()\nplt.savefig(output_dir / 'family_size_analysis.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 7. Correlation Heatmap\nfig, ax = plt.subplots(figsize=(10, 8))\n\n# Select numeric columns\nnumeric_cols = ['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\ncorr_df = df[numeric_cols].corr()\n\n# Create heatmap\nmask = np.triu(np.ones_like(corr_df, dtype=bool))\nsns.heatmap(corr_df, mask=mask, annot=True, fmt='.2f', cmap='coolwarm',\n center=0, square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.8}, ax=ax)\nax.set_title('Correlation Heatmap of Titanic Dataset Features')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'correlation_heatmap.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 8. Combined Factors: Class, Gender, and Survival\nfig, ax = plt.subplots(figsize=(10, 6))\n\n# Create a grouped bar chart\nclass_gender_data = df.groupby(['Pclass', 'Sex'])['Survived'].mean().unstack() * 100\nclass_gender_data.plot(kind='bar', ax=ax, color=['#ff6b6b', '#4ecdc4'])\nax.set_title('Survival Rate by Class and Gender')\nax.set_xlabel('Passenger Class')\nax.set_ylabel('Survival Rate (%)')\nax.set_ylim(0, 105)\nax.legend(title='Gender')\nax.grid(True, alpha=0.3)\n\n# Add value labels\nfor i, (idx, row) in enumerate(class_gender_data.iterrows()):\n for j, (col, value) in enumerate(row.items()):\n if not pd.isna(value):\n ax.text(i + j*0.15 - 0.15, value + 2, f'{value:.1f}%', \n ha='center', fontsize=9)\n\nplt.tight_layout()\nplt.savefig(output_dir / 'class_gender_survival.png', dpi=150, bbox_inches='tight')\nplt.close()\n\nprint(f\"Visualizations saved to: {output_dir}\")\nprint(f\"Generated {len(list(output_dir.glob('*.png')))} charts\")"
+ },
+ "id": "call_ffn7cu7r1buz2s7r2bwmzvcu",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "OK",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "write_file",
+ "id": "fe372950-5cdf-4cb3-88c8-1a1b0f5f56c2",
+ "tool_call_id": "call_ffn7cu7r1buz2s7r2bwmzvcu",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": ""
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cb-ebb4-75e1-bedf-2b52792a093a",
+ "tool_calls": [
+ {
+ "name": "bash",
+ "args": {
+ "description": "Run visualization script",
+ "command": "cd /mnt/user-data/workspace && python visualize_titanic.py"
+ },
+ "id": "call_pq1p48mo0rc3p2tzxm6u6y9g",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "Traceback (most recent call last):\n File \"/mnt/user-data/workspace/visualize_titanic.py\", line 112, in \n sns.boxplot(x='Survived', y='Fare', data=df, ax=axes[0],\n File \"/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\", line 1626, in boxplot\n p.map_hue(palette=palette, order=hue_order, norm=hue_norm, saturation=saturation)\n File \"/usr/local/lib/python3.10/dist-packages/seaborn/_base.py\", line 838, in map_hue\n mapping = HueMapping(self, palette, order, norm, saturation)\n File \"/usr/local/lib/python3.10/dist-packages/seaborn/_base.py\", line 150, in __init__\n levels, lookup_table = self.categorical_mapping(\n File \"/usr/local/lib/python3.10/dist-packages/seaborn/_base.py\", line 234, in categorical_mapping\n raise ValueError(err.format(missing))\nValueError: The palette dictionary is missing keys: {'1', '0'}",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "bash",
+ "id": "4006e44c-8578-4bca-a0fa-f16dfa863336",
+ "tool_call_id": "call_pq1p48mo0rc3p2tzxm6u6y9g",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Let me fix the visualization script. The issue is with the palette dictionary format. Let me correct it."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cc-13f2-7843-841e-282dc2d02077",
+ "tool_calls": [
+ {
+ "name": "str_replace",
+ "args": {
+ "description": "Fix palette dictionary in visualization script",
+ "path": "/mnt/user-data/workspace/visualize_titanic.py",
+ "old_str": "# Box plot of fare by survival\nsns.boxplot(x='Survived', y='Fare', data=df, ax=axes[0], \n palette={0: '#ff6b6b', 1: '#4ecdc4'})\naxes[0].set_title('Fare Distribution by Survival')\naxes[0].set_xlabel('Survived (0=No, 1=Yes)')\naxes[0].set_ylabel('Fare ($)')\naxes[0].set_xticklabels(['Perished', 'Survived'])\n\n# Fare by class and survival\nsns.boxplot(x='Pclass', y='Fare', hue='Survived', data=df, ax=axes[1],\n palette={0: '#ff6b6b', 1: '#4ecdc4'})\naxes[1].set_title('Fare by Class and Survival')\naxes[1].set_xlabel('Passenger Class')\naxes[1].set_ylabel('Fare ($)')\naxes[1].legend(title='Survived', labels=['Perished', 'Survived'])",
+ "new_str": "# Box plot of fare by survival\nsns.boxplot(x='Survived', y='Fare', data=df, ax=axes[0], \n palette=['#ff6b6b', '#4ecdc4'])\naxes[0].set_title('Fare Distribution by Survival')\naxes[0].set_xlabel('Survived (0=No, 1=Yes)')\naxes[0].set_ylabel('Fare ($)')\naxes[0].set_xticklabels(['Perished', 'Survived'])\n\n# Fare by class and survival\nsns.boxplot(x='Pclass', y='Fare', hue='Survived', data=df, ax=axes[1],\n palette=['#ff6b6b', '#4ecdc4'])\naxes[1].set_title('Fare by Class and Survival')\naxes[1].set_xlabel('Passenger Class')\naxes[1].set_ylabel('Fare ($)')\naxes[1].legend(title='Survived', labels=['Perished', 'Survived'])"
+ },
+ "id": "call_coijhabwevm34rhr2acg8pca",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "OK",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "str_replace",
+ "id": "3003236c-9ae3-4ec5-be94-faa8d17327bf",
+ "tool_call_id": "call_coijhabwevm34rhr2acg8pca",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": ""
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cc-70d3-7bb2-8e5f-39e773ee45f3",
+ "tool_calls": [
+ {
+ "name": "bash",
+ "args": {
+ "description": "Run fixed visualization script",
+ "command": "cd /mnt/user-data/workspace && python visualize_titanic.py"
+ },
+ "id": "call_olncm3wpicf49t3dfyql7z7j",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "Visualizations saved to: /mnt/user-data/workspace/visualizations\nGenerated 7 charts",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "bash",
+ "id": "fefb94c3-f8bc-4c83-bad7-cac3e3896542",
+ "tool_call_id": "call_olncm3wpicf49t3dfyql7z7j",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Now let me create a comprehensive report with the findings. First, let me copy the summary and visualizations to the output directory."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cc-99da-75d3-9a03-94606f814539",
+ "tool_calls": [
+ {
+ "name": "bash",
+ "args": {
+ "description": "Copy analysis results to output directory",
+ "command": "mkdir -p /mnt/user-data/outputs && cp /mnt/user-data/workspace/titanic_summary.txt /mnt/user-data/outputs/ && cp -r /mnt/user-data/workspace/visualizations /mnt/user-data/outputs/"
+ },
+ "id": "call_9ewxwg2uun7ucbq3bfqhm9he",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "(no output)",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "bash",
+ "id": "af30c03d-8b0d-46e0-b7de-3cb3a038c84a",
+ "tool_call_id": "call_9ewxwg2uun7ucbq3bfqhm9he",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Now I need to present the findings. Let me read the summary file and provide a comprehensive analysis."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cc-c9bd-7760-8af0-fc9a02080c1e",
+ "tool_calls": [
+ {
+ "name": "read_file",
+ "args": {
+ "description": "Read the summary file to present findings",
+ "path": "/mnt/user-data/outputs/titanic_summary.txt"
+ },
+ "id": "call_nuod544uawfl4b66y3ebzw2w",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "=== TITANIC DATASET ANALYSIS SUMMARY ===\n\nDataset shape: (891, 20)\nTotal passengers: 891\nSurvivors: 342 (38.38%)\n\nKEY FINDINGS:\n1. Gender disparity: Female survival rate was much higher than male.\n - Female: 74.20%\n - Male: 18.89%\n\n2. Class disparity: Higher classes had better survival rates.\n - Class 1: 62.96% (136/216)\n - Class 2: 47.28% (87/184)\n - Class 3: 24.24% (119/491)\n\n3. Children had better survival rates than adults.\n - Child (0-12): 57.97% (40/69)\n - Teen (13-18): 42.86% (30/70)\n - Young Adult (19-30): 35.56% (96/270)\n - Adult (31-50): 42.32% (102/241)\n - Senior (51+): 34.38% (22/64)\n\n4. Passengers with cabins had much higher survival rates.\n - With cabin: 66.67%\n - Without cabin: 29.99%\n\n5. Family size affected survival.\n - Alone: 30.35%\n - With family: 50.56%\n\n6. Embarkation port correlated with survival.\n - Port C: 55.36% (93/168)\n - Port Q: 38.96% (30/77)\n - Port S: 33.70% (217/644)\n",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "read_file",
+ "id": "47e53865-18cd-4304-b872-087b36c585a4",
+ "tool_call_id": "call_nuod544uawfl4b66y3ebzw2w",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "",
+ "additional_kwargs": {
+ "reasoning_content": "Now I'll present the findings from the Titanic dataset analysis. Let me also check what visualizations were created."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cc-ed2a-76d2-87ac-5458fbad1908",
+ "tool_calls": [
+ {
+ "name": "ls",
+ "args": {
+ "description": "List generated visualizations",
+ "path": "/mnt/user-data/outputs/visualizations"
+ },
+ "id": "call_wghrbpjy1afgclgsdd5sbvqj",
+ "type": "tool_call"
+ }
+ ],
+ "invalid_tool_calls": [],
+ "usage_metadata": null
+ },
+ {
+ "content": "/mnt/user-data/outputs/visualizations\n/mnt/user-data/outputs/visualizations/survival_by_class.png\n/mnt/user-data/outputs/visualizations/fare_analysis.png\n/mnt/user-data/outputs/visualizations/class_gender_survival.png\n/mnt/user-data/outputs/visualizations/family_size_analysis.png\n/mnt/user-data/outputs/visualizations/correlation_heatmap.png\n/mnt/user-data/outputs/visualizations/survival_by_age.png\n/mnt/user-data/outputs/visualizations/survival_overview.png",
+ "additional_kwargs": {},
+ "response_metadata": {},
+ "type": "tool",
+ "name": "ls",
+ "id": "d56d6486-238e-4d16-a716-0c40e5f9d4a3",
+ "tool_call_id": "call_wghrbpjy1afgclgsdd5sbvqj",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "Based on my analysis of the Titanic dataset (891 passengers), here are the most interesting findings:\n\n## **Key Survival Patterns**\n\n### **1. Dramatic Gender Disparity**\n- **Female survival rate: 74.2%** (233 out of 314 women survived)\n- **Male survival rate: 18.9%** (109 out of 577 men survived)\n- This confirms the \"women and children first\" protocol was strongly followed\n\n### **2. Strong Class Hierarchy**\n- **First Class: 63.0%** survival rate (136/216)\n- **Second Class: 47.3%** survival rate (87/184) \n- **Third Class: 24.2%** survival rate (119/491)\n- Higher socioeconomic status significantly increased survival chances\n\n### **3. Age Matters, But Not Uniformly**\n- **Children (0-12): 58.0%** survival - highest among age groups\n- **Teens (13-18): 42.9%** survival\n- **Young Adults (19-30): 35.6%** survival\n- **Adults (31-50): 42.3%** survival\n- **Seniors (51+): 34.4%** survival\n\n### **4. Cabin Privilege**\n- **With cabin: 66.7%** survival rate (136/204)\n- **Without cabin: 30.0%** survival rate (206/687)\n- Having a cabin (typically upper decks) nearly doubled survival chances\n\n## **Interesting Interactions**\n\n### **Class × Gender Combination**\n- **First Class Women: 96.8%** survival (91/94) - nearly guaranteed survival\n- **Third Class Men: 13.5%** survival (47/347) - worst odds\n- **First Class Men: 36.9%** survival (45/122) - better than average male\n\n### **Family Dynamics**\n- **Alone: 30.4%** survival (163/537)\n- **With family: 50.6%** survival (179/354)\n- **Optimal family size: 4 members** with 72.4% survival rate\n- Large families (6+ members) had poor survival rates\n\n### **Embarkation Port Correlation**\n- **Cherbourg (C): 55.4%** survival - highest\n- **Queenstown (Q): 39.0%** survival \n- **Southampton (S): 33.7%** survival - lowest\n- Port may correlate with passenger class composition\n\n## **Statistical Insights**\n\n### **Fare Analysis**\n- **Average fare: $32.20** (median: $14.45)\n- **Class 1 average: $84.15** vs **Class 3 average: $13.68**\n- Survivors paid significantly higher fares on average\n\n### **Title Analysis**\n- **\"Miss\": 69.8%** survival (127/182)\n- **\"Mrs\": 79.2%** survival (99/125) \n- **\"Mr\": 15.7%** survival (81/517)\n- **\"Master\" (boys): 57.5%** survival (23/40)\n- Titles reveal social status and age information\n\n### **Missing Data Patterns**\n- **Age: 19.9%** missing (177 passengers)\n- **Cabin: 77.1%** missing (687 passengers) - mostly 3rd class\n- **Embarked: 0.2%** missing (2 passengers)\n\n## **Most Surprising Findings**\n\n1. **The \"First Class Female\" advantage**: Nearly all first-class women survived (96.8%), suggesting class and gender combined for near-certain survival.\n\n2. **Family size sweet spot**: Families of 4 had the highest survival rate (72.4%), while very large families fared poorly.\n\n3. **Cabin vs No-Cabin gap**: The survival difference (66.7% vs 30.0%) suggests cabin location was critical, likely due to proximity to lifeboats.\n\n4. **Port C advantage**: Cherbourg passengers had significantly better survival rates, possibly due to different passenger demographics.\n\n5. **\"Master\" title survival**: Boys with the \"Master\" title had 57.5% survival - much higher than adult males, supporting the \"children first\" protocol.\n\nThe dataset reveals a clear hierarchy: **First Class Women > First Class Men/Children > Second Class > Third Class Women/Children > Third Class Men**, with socioeconomic factors playing a decisive role in survival outcomes.\n\n",
+ "additional_kwargs": {
+ "reasoning_content": "Now I'll present my findings from the Titanic dataset analysis. Let me create a comprehensive response summarizing the interesting discoveries."
+ },
+ "response_metadata": {
+ "finish_reason": "tool_calls",
+ "model_name": "deepseek-v3-2-251201",
+ "service_tier": "default",
+ "model_provider": "deepseek"
+ },
+ "type": "ai",
+ "name": null,
+ "id": "lc_run--019bf0cd-16fa-7432-a728-b8a071167a56",
+ "tool_calls": [
+ {
+ "name": "present_files",
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+ "/mnt/user-data/outputs/titanic_summary.txt",
+ "/mnt/user-data/outputs/visualizations/survival_overview.png",
+ "/mnt/user-data/outputs/visualizations/survival_by_class.png",
+ "/mnt/user-data/outputs/visualizations/class_gender_survival.png",
+ "/mnt/user-data/outputs/visualizations/survival_by_age.png",
+ "/mnt/user-data/outputs/visualizations/fare_analysis.png",
+ "/mnt/user-data/outputs/visualizations/family_size_analysis.png",
+ "/mnt/user-data/outputs/visualizations/correlation_heatmap.png"
+ ]
+ },
+ "id": "call_zgfi5cqd5691kd4v5u672h9b",
+ "type": "tool_call"
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+ "invalid_tool_calls": [],
+ "usage_metadata": null
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+ "content": "Successfully presented files",
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+ "tool_call_id": "call_zgfi5cqd5691kd4v5u672h9b",
+ "artifact": null,
+ "status": "success"
+ },
+ {
+ "content": "I've analyzed the Titanic dataset and identified several compelling findings. The analysis reveals clear survival hierarchies based on gender, class, age, and socioeconomic factors. \n\nThe most striking patterns include the dramatic gender disparity (74.2% female vs 18.9% male survival), strong class hierarchy (63% first class vs 24% third class survival), and the advantage of having a cabin (66.7% vs 30.0% survival). \n\nI've provided a detailed summary file and seven visualizations that illustrate these patterns, including survival rates by class and gender, age distributions, fare analysis, family size impacts, and correlation patterns. The visualizations clearly show how different factors interacted to determine survival outcomes during the Titanic disaster.",
+ "response_metadata": {
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+ },
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+ "size": 60302,
+ "path": "/mnt/user-data/uploads/titanic.csv",
+ "extension": ".csv"
+ }
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+ "title": "Analyzing Dataset Insights",
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+ "/mnt/user-data/outputs/visualizations/survival_overview.png",
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+ "/mnt/user-data/outputs/visualizations/family_size_analysis.png",
+ "/mnt/user-data/outputs/visualizations/correlation_heatmap.png"
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diff --git a/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/outputs/titanic_summary.txt b/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/outputs/titanic_summary.txt
new file mode 100644
index 0000000..1879f88
--- /dev/null
+++ b/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/outputs/titanic_summary.txt
@@ -0,0 +1,35 @@
+=== TITANIC DATASET ANALYSIS SUMMARY ===
+
+Dataset shape: (891, 20)
+Total passengers: 891
+Survivors: 342 (38.38%)
+
+KEY FINDINGS:
+1. Gender disparity: Female survival rate was much higher than male.
+ - Female: 74.20%
+ - Male: 18.89%
+
+2. Class disparity: Higher classes had better survival rates.
+ - Class 1: 62.96% (136/216)
+ - Class 2: 47.28% (87/184)
+ - Class 3: 24.24% (119/491)
+
+3. Children had better survival rates than adults.
+ - Child (0-12): 57.97% (40/69)
+ - Teen (13-18): 42.86% (30/70)
+ - Young Adult (19-30): 35.56% (96/270)
+ - Adult (31-50): 42.32% (102/241)
+ - Senior (51+): 34.38% (22/64)
+
+4. Passengers with cabins had much higher survival rates.
+ - With cabin: 66.67%
+ - Without cabin: 29.99%
+
+5. Family size affected survival.
+ - Alone: 30.35%
+ - With family: 50.56%
+
+6. Embarkation port correlated with survival.
+ - Port C: 55.36% (93/168)
+ - Port Q: 38.96% (30/77)
+ - Port S: 33.70% (217/644)
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diff --git a/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/uploads/titanic.csv b/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/uploads/titanic.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/uploads/titanic.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
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+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
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+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
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+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
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+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
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+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/frontend/src/components/workspace/messages/message-list.tsx b/frontend/src/components/workspace/messages/message-list.tsx
index 1795daf..d74f596 100644
--- a/frontend/src/components/workspace/messages/message-list.tsx
+++ b/frontend/src/components/workspace/messages/message-list.tsx
@@ -4,11 +4,15 @@ import {
Conversation,
ConversationContent,
} from "@/components/ai-elements/conversation";
+import { MessageResponse } from "@/components/ai-elements/message";
import {
+ extractContentFromMessage,
extractPresentFilesFromMessage,
groupMessages,
+ hasContent,
hasPresentFiles,
} from "@/core/messages/utils";
+import { useRehypeSplitWordsIntoSpans } from "@/core/rehype";
import type { AgentThreadState } from "@/core/threads";
import { cn } from "@/lib/utils";
@@ -30,6 +34,7 @@ export function MessageList({
thread: UseStream;
paddingBottom?: number;
}) {
+ const rehypePlugins = useRehypeSplitWordsIntoSpans(thread.isLoading);
if (thread.isThreadLoading) {
return ;
}
@@ -57,11 +62,17 @@ export function MessageList({
}
}
return (
-
+