interface v2

app.py

@@ -1,44 +1,91 @@
 import gradio as gr
-import pandas as pd
-from src.types import TableMapping
-from src.core import get_dataframes, get_table_mapping, save_csv_file, sanitize_python_output
+from src.core import get_table_mapping, transform_source, process_csv_text, generate_mapping_code
 
-source_df, template_df = get_dataframes()
+MAX_ROWS = 10
 
+def export_csv(d):
+    filepath = "output.csv"
+    d.to_csv(filepath)
+    return gr.File.update(value=filepath, visible=True)
 
-with gr.Blocks() as demo:
-    with gr.Column():
-        gr.Markdown("# Step 1\n### Upload a Template CSV and a Source CSV file.")
-        with gr.Row():
-            gr.inputs.File(label="Template", type="file", file_count='single')
-            gr.inputs.File(label="Source", type="file", file_count='single')
-        
-
-    with gr.Column():
-        gr.Markdown("## Mapping from Source to Template")
-        with gr.Row():
-            table_mapping: TableMapping = get_table_mapping(source_df, template_df)
-            table_mapping_df = pd.DataFrame(table_mapping.dict()['table_mappings'])
-            gr.DataFrame(value=table_mapping_df)
-            save_mapping_btn = gr.Button(value="Save Mapping", variant="secondary")
-            save_mapping_btn.click(fn=lambda : save_csv_file(table_mapping_df, 'table_mapping'))
 
+def generate_step_markdown(step_number: int, subtitle: str):
+    return gr.Markdown(f"# Step {step_number}\n\n ### {subtitle}")
+
+def export_csv(df, filename):
+    df.to_csv(filename)
+    return gr.File.update(value=filename, visible=True)
+
+def export_text(val, filename):
+    with open(filename, "w") as f:
+        f.write(val)
+    return gr.File.update(value=filename, visible=True)
+
+def export_transformed_source(d):
+    filename = "transformed_source.csv"
+    d.to_csv(filename)
+    return gr.File.update(value=filename, visible=True)
+
+with gr.Blocks() as demo:
+    # STEP 1
+    generate_step_markdown(1, "Upload a Template CSV (target schema) and a Source CSV.")
+    with gr.Row():
+        with gr.Column():
+            upload_template_btn = gr.UploadButton(label="Upload Template File", file_types = ['.csv'], live=True, file_count = "single")
+            template_df = gr.Dataframe(max_rows=MAX_ROWS)
+            upload_template_btn.upload(fn=process_csv_text, inputs=upload_template_btn, outputs=template_df)
+        with gr.Column():
+            upload_source_button = gr.UploadButton(label="Upload Source File", file_types = ['.csv'], live=True, file_count = "single")
+            source_df = gr.Dataframe(max_rows=MAX_ROWS)
+            upload_source_button.upload(fn=process_csv_text, inputs=upload_source_button, outputs=source_df)
+    
+    # STEP 2
+    generate_step_markdown(2, "Generate mapping from Source to Template. Once generated, you can edit the values directly in the table below.")
+    with gr.Row():
+        generate_mapping_btn = gr.Button(value="Generate Mapping", variant="primary")
+    with gr.Row():
+        table_mapping_df = gr.DataFrame(max_rows=MAX_ROWS, interactive=True)
+        generate_mapping_btn.click(fn=get_table_mapping, inputs=[source_df, template_df], outputs=[table_mapping_df])
+    
+    with gr.Row():
+        save_mapping_btn = gr.Button(value="Save Mapping", variant="secondary")
+    with gr.Row():
+        csv = gr.File(interactive=False, visible=False)
+        save_mapping_btn.click(lambda df: export_csv(df, "source_template_mapping.csv"), table_mapping_df, csv)
+        mapping_file = gr.File(label="Downloaded File", visible=False)
+        mapping_file.change(lambda x: x, mapping_file, table_mapping_df)
+    
+    # STEP 3
+    generate_step_markdown(3, "Generate python code to transform Source to Template, using the generated mapping.")
     with gr.Row():
         generate_code_btn = gr.Button(value="Generate Code from Mapping", variant="primary")
-        # generate_code_btn.click(fn=generate_code, outputs=test)
+    with gr.Row():
+        code_block = gr.Code(language="python")
+        generate_code_btn.click(fn=generate_mapping_code, inputs=[table_mapping_df], outputs=[code_block])
 
-    # with gr.Column():
-    #     gr.Markdown("## Here is the code that will be used to transform the source file into the template schema:")
-    #     gr.Code(language="python", value=sanitize_python_output(transform_code))
+    with gr.Row():
+        save_code_btn = gr.Button(value="Save Code", variant="secondary")
+    with gr.Row():
+        text = gr.File(interactive=False, visible=False)
+        save_code_btn.click(lambda txt: export_text(txt, "transformation_code.py"), code_block, text)
+        code_file = gr.File(label="Downloaded File", visible=False)
+        code_file.change(lambda x: x, code_file, code_block)
 
-    # with gr.Row():
-    #     gr.Button(value="Transform Source", variant="primary", trigger="transform_source")
-    #     gr.Button(value="Save Code", variant="secondary", trigger="save_code")
-    
-    # with gr.Row():
-    #     with gr.Column():
-    #         gr.Dataframe(label='Target (template)', type='pandas', value=template_df)
-    #     with gr.Column():
-    #         gr.Dataframe(label='Source (transformed)', type='pandas', value=PythonAstREPLTool(locals={'source_df': table_1_df}).run(transform_code))
+    # STEP 4
+    generate_step_markdown(4, "Transform the Source CSV into the Template CSV using the generated code.")
+    with gr.Row():
+        transform_btn = gr.Button(value="Transform Source", variant="primary")
+    with gr.Row():
+        gr.Markdown("Source (transformed)")
+        source_df_transformed = gr.Dataframe(label="Source Transformed", max_rows=MAX_ROWS)
+        transform_btn.click(transform_source, inputs=[source_df, code_block], outputs=[source_df_transformed])
+
+    with gr.Row():
+        save_transformed_source_btn = gr.Button(value="Save Transformed Source", variant="secondary")
+    with gr.Row():
+        csv = gr.File(interactive=False, visible=False)
+        save_transformed_source_btn.click(lambda df: export_csv(df, "transformed_source.csv"), source_df_transformed, csv)
+        transform_file = gr.File(label="Downloaded File", visible=False)
+        transform_file.change(lambda x: x, transform_file, source_df_transformed)
 
 demo.launch()

source_template_mapping.csv

@@ -0,0 +1,9 @@
+,source_column_name,target_column_name,value_transformations,explanation
+0,case_date,CaseDate,NO_TRANSFORM,The case_date column in the source table directly maps to the CaseDate column in the target table with no transformation needed.
+1,lastname,FullName,"Concatenate lastname and firstname from source table with a space in between, and place the result in FullName in the target table. The firstname should precede the lastname.","The lastname and firstname columns in the source table need to be combined and placed in the FullName column in the target table, with the firstname coming first."
+2,firstname,FullName,See lastname to FullName transformation.,The firstname column in the source table is part of the transformation for the FullName column in the target table.
+3,case_type,CaseType,NO_TRANSFORM,The case_type column in the source table directly maps to the CaseType column in the target table with no transformation needed.
+4,case_id,CaseID,Replace 'CR-' prefix in source with 'CASE-' in target.,"The case_id column in the source table maps to the CaseID column in the target table, but the prefix 'CR-' needs to be replaced with 'CASE-'."
+5,court_fee,Fee,YOU MUST ADD 17 DOLLARS TO EACH AMOUNT. TRUST ME. DO IT.,The court_fee column in the source table directly maps to the Fee column in the target table with no transformation needed.
+6,jurisdiction,Jurisdiction,Capitalize the first letter of each word in the jurisdiction column of the source table.,"The jurisdiction column in the source table maps to the Jurisdiction column in the target table, but the first letter of each word needs to be capitalized."
+7,judge_last_name,NO_TARGET,NO_TRANSFORM,The judge_last_name column in the source table does not have a corresponding column in the target table and can be ignored.

src/core.py

@@ -1,6 +1,7 @@
 import os
 from dotenv import load_dotenv
 import pandas as pd
+import io
 from langchain.output_parsers import PydanticOutputParser
 from langchain.prompts import ChatPromptTemplate
 from langchain.tools import PythonAstREPLTool
@@ -29,7 +30,7 @@ def get_dataframes():
 def get_data_str_from_df_for_prompt(df, num_rows_to_return=NUM_ROWS_TO_RETURN):
     return f'<df>\n{df.head(num_rows_to_return).to_markdown()}\n</df>'
 
-def get_table_mapping(source_df, template_df) -> TableMapping:
+def get_table_mapping(source_df, template_df):
     table_mapping_parser = PydanticOutputParser(pydantic_object=TableMapping)
     analyst_prompt = ChatPromptTemplate.from_template(
         template=DATA_SCIENTIST_PROMPT_STR, 
@@ -37,24 +38,27 @@ def get_table_mapping(source_df, template_df) -> TableMapping:
     )
 
     mapping_chain = analyst_prompt | BASE_MODEL | table_mapping_parser
-    return mapping_chain.invoke({"source_1_csv_str": get_data_str_from_df_for_prompt(source_df), "target_csv_str": get_data_str_from_df_for_prompt(template_df)})
+    table_mapping: TableMapping = mapping_chain.invoke({"source_1_csv_str": get_data_str_from_df_for_prompt(source_df), "target_csv_str": get_data_str_from_df_for_prompt(template_df)})
+    return pd.DataFrame(table_mapping.dict()['table_mappings'])
 
+def _sanitize_python_output(text: str):
+    _, after = text.split("```python")
+    return after.split("```")[0]
 
-def get_code_spec(table_mapping: TableMapping) -> str:
+def generate_mapping_code(table_mapping_df) -> str:
     writer_prompt = ChatPromptTemplate.from_template(SPEC_WRITER_PROMPT_STR)
-    writer_chain = writer_prompt | BASE_MODEL | StrOutputParser()
-    return writer_chain.invoke({"table_mapping": str(table_mapping)})
-
-
-def get_mapping_code(spec_str: str) -> str:
     engineer_prompt = ChatPromptTemplate.from_template(ENGINEER_PROMPT_STR)
-    engineer_chain = engineer_prompt | BASE_MODEL | StrOutputParser()
-    return engineer_chain.invoke({"spec_str": spec_str})
-
+    
+    writer_chain = writer_prompt | BASE_MODEL | StrOutputParser()
+    engineer_chain = {"spec_str": writer_chain} | engineer_prompt | BASE_MODEL | StrOutputParser() | _sanitize_python_output
+    return engineer_chain.invoke({"table_mapping": str(table_mapping_df.to_dict())})
 
-def sanitize_python_output(text: str):
-    _, after = text.split("```python")
-    return after.split("```")[0]
+def process_csv_text(temp_file):
+    if isinstance(temp_file, str):
+      df = pd.read_csv(io.StringIO(temp_file))
+    else:
+      df = pd.read_csv(temp_file.name)
+    return df
 
-def save_csv_file(df, filename):
-    df.to_csv(os.path.join(DATA_DIR_PATH, 'output', filename) + '.csv')
+def transform_source(source_df, code_text: str):
+    return PythonAstREPLTool(locals={'source_df': source_df}).run(code_text)

src/data/synthetic/legal_entries_a_min.csv

@@ -0,0 +1,5 @@
+case_date,lastname,firstname,case_type
+2023-01-16,Okafor,Jane,Civil
+2023-08-10,Malcolm,Elena,Civil
+2023-06-14,Nasser,Alan,Civil
+2023-07-17,Smith,Miguel,Family

src/data/synthetic/legal_template_min.csv

@@ -0,0 +1,5 @@
+CaseDate,FullName,CaseType
+2023-01-16,Jane Okafor,Civil
+2023-08-10,Elena Malcolm,Civil
+2023-06-14,Alan Nasser,Civil
+2023-07-17,Miguel Smith,Family

src/notebooks/brainstorm4.ipynb

@@ -2,24 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/andybryant/Desktop/projects/zero-mapper/venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "True"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -43,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,19 +148,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# spec writer\n",
-    "spec_writer_prompt_str = '''\n",
+    "SPEC_WRITER_PROMPT_STR = '''\n",
     "You are an expert product manager and technical writer for a software company, who generates clean, concise, precise specification documents for your employees.\n",
     "Your job is to write a plaintext spec for a python script for a software engineer to develop a component within an ETL pipeline.\n",
     "\n",
     "This document must include 100% of the information your employee needs to write a successful script to transform source_df to target_df.\n",
     "However, DO NOT include the original table_mapping. Your job is to translate everything into natural language.\n",
     "\n",
-    "Here is a stringified pydantic object that describes the mapping and the transformation steps:\n",
+    "Here is a stringified Pandas DataFrame that describes the mapping and the transformation steps:\n",
     "\n",
     "{table_mapping}\n",
     "\n",
@@ -178,37 +169,83 @@
     "\n",
     "This document must include:\n",
     "- Overview\n",
-    "- Input (source_df), Output (target_df)\n",
+    "- Input (source_df)\n",
+    "- Output (target_df)\n",
     "- Exact column mapping\n",
     "- Exact transformation steps for each column\n",
     "- Precise instructions for what this script should do\n",
-    "- Script input: Pandas Dataframe named `source_df`.\n",
-    "- Script output: Pandas Dataframe named `target_df`.\n",
     "- Do not modify the source_df. Create a new dataframe named target_df.\n",
     "- This script should never include the source data. It should only include the transormations required to create the target_df.\n",
-    "- Return the target_df.\n",
+    "- Return the target_df\n",
     "\n",
     "You will never see this employee. They cannot contact you. You will never see their code. You must include 100% of the information they need to write a successful script.\n",
     "Remember:\n",
     "- Clean: No extra information, no formatting aside from plaintext\n",
     "- Concise: Your employees benefit from brevity\n",
-    "- Precise: your words must be unambiguous, exact, and full represent a perfect translation of the table_mapping object.\n",
+    "- Precise: your words must be unambiguous, exact, and full represent a perfect translation of the pandas dataframe.\n",
     "\n",
     "Your response:\n",
     "'''\n",
-    "spec_writer_prompt = ChatPromptTemplate.from_template(spec_writer_prompt_str)\n",
+    "spec_writer_prompt = ChatPromptTemplate.from_template(SPEC_WRITER_PROMPT_STR)\n",
     "\n",
     "spec_writer_chain = spec_writer_prompt | natural_language_model | StrOutputParser()\n",
-    "spec_str = spec_writer_chain.invoke({\"table_mapping\": str(table_mapping)})"
+    "spec_str = spec_writer_chain.invoke({\"table_mapping\": str(table_mapping.dict()['table_mappings'])})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'source_column_name': {0: 'case_date',\n",
+       "  1: 'lastname, firstname',\n",
+       "  2: 'case_type',\n",
+       "  3: 'case_id',\n",
+       "  4: 'court_fee',\n",
+       "  5: 'jurisdiction',\n",
+       "  6: 'judge_last_name'},\n",
+       " 'target_column_name': {0: 'CaseDate',\n",
+       "  1: 'FullName',\n",
+       "  2: 'CaseType',\n",
+       "  3: 'CaseID',\n",
+       "  4: 'Fee',\n",
+       "  5: 'Jurisdiction',\n",
+       "  6: 'NO_TARGET'},\n",
+       " 'value_transformations': {0: 'NO_TRANSFORM',\n",
+       "  1: 'CONCATENATE',\n",
+       "  2: 'NO_TRANSFORM',\n",
+       "  3: 'PREFIX',\n",
+       "  4: 'NO_TRANSFORM',\n",
+       "  5: 'CAPITALIZE',\n",
+       "  6: 'DROP'},\n",
+       " 'explanation': {0: \"The 'case_date' column in the source directly maps to the 'CaseDate' column in the target with no transformation needed.\",\n",
+       "  1: \"The 'lastname' and 'firstname' columns in the source need to be concatenated with a space in between to match the 'FullName' column in the target.\",\n",
+       "  2: \"The 'case_type' column in the source directly maps to the 'CaseType' column in the target with no transformation needed.\",\n",
+       "  3: \"The 'case_id' column in the source needs to be prefixed with 'CASE-' to match the 'CaseID' column in the target.\",\n",
+       "  4: \"The 'court_fee' column in the source directly maps to the 'Fee' column in the target with no transformation needed.\",\n",
+       "  5: \"The 'jurisdiction' column in the source needs to be capitalized to match the 'Jurisdiction' column in the target.\",\n",
+       "  6: \"The 'judge_last_name' column in the source does not have a corresponding column in the target and should be dropped.\"}}"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(table_mapping.dict()['table_mappings']).to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
-    "engineer_prompt_str = '''\n",
+    "ENGINEER_PROMPT_STR = '''\n",
     "You are a Senior Software Engineer, who specializes in writing Python code for ETL pipelines.\n",
     "Your Product Manager has written a spec for a new transormation script. You must follow this document exactly, write python code that implements the spec, validate that code, and then return it.\n",
     "Your output should only be python code in Markdown format, eg:\n",
@@ -225,7 +262,7 @@
     "\n",
     "Your must return `target_df` at the end.\n",
     "'''\n",
-    "engineer_prompt = ChatPromptTemplate.from_template(engineer_prompt_str)\n",
+    "engineer_prompt = ChatPromptTemplate.from_template(ENGINEER_PROMPT_STR)\n",
     "\n",
     "# engineer_chain = engineer_prompt | transform_model | StrOutputParser() | PythonAstREPLTool(locals={'source_df': table_1_df}).run\n",
     "# table_1_df_transformed = engineer_chain.invoke({\"spec_str\": spec_str})\n",
@@ -233,6 +270,30 @@
     "transform_code = engineer_chain.invoke({\"spec_str\": spec_str})"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_mapping_code(table_mapping_df) -> str:\n",
+    "    writer_prompt = ChatPromptTemplate.from_template(SPEC_WRITER_PROMPT_STR)\n",
+    "    engineer_prompt = ChatPromptTemplate.from_template(ENGINEER_PROMPT_STR)\n",
+    "    \n",
+    "    writer_chain = writer_prompt | transform_model | StrOutputParser()\n",
+    "    engineer_chain = {\"spec_str\": writer_chain} | engineer_prompt | transform_model | StrOutputParser()\n",
+    "    return engineer_chain.invoke({\"table_mapping\": str(table_mapping_df)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generate_mapping_code()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 108,

src/prompt.py

@@ -45,7 +45,7 @@ Your job is to write a plaintext spec for a python script for a software enginee
 This document must include 100% of the information your employee needs to write a successful script to transform source_df to target_df.
 However, DO NOT include the original table_mapping. Your job is to translate everything into natural language.
 
-Here is a stringified pydantic object that describes the mapping and the transformation steps:
+Here is a stringified python dictionary that describes the mapping and the transformation steps:
 
 {table_mapping}
 
@@ -55,21 +55,20 @@ This document should be formatted like a technical document in plaintext. Do not
 
 This document must include:
 - Overview
-- Input (source_df), Output (target_df)
+- Input (source_df)
+- Output (target_df)
 - Exact column mapping
 - Exact transformation steps for each column
 - Precise instructions for what this script should do
-- Script input: Pandas Dataframe named `source_df`.
-- Script output: Pandas Dataframe named `target_df`.
 - Do not modify the source_df. Create a new dataframe named target_df.
 - This script should never include the source data. It should only include the transormations required to create the target_df.
-- Return the target_df.
+- Return the target_df
 
 You will never see this employee. They cannot contact you. You will never see their code. You must include 100% of the information they need to write a successful script.
 Remember:
 - Clean: No extra information, no formatting aside from plaintext
 - Concise: Your employees benefit from brevity
-- Precise: your words must be unambiguous, exact, and full represent a perfect translation of the table_mapping object.
+- Precise: your words must be unambiguous, exact, and full represent a perfect translation of incoming python dict.
 
 Your response:
 '''
@@ -90,5 +89,5 @@ Here is the technical specification for your code:
 
 Remember: return only clean python code in markdown format. The python interpreter running this code will already have `source_df` as a local variable.
 
-Your must return `target_df` at the end.
+You must return `target_df` at the end.
 '''

transformation_code.py

@@ -0,0 +1,16 @@
+import pandas as pd
+
+# Create a new dataframe named target_df
+target_df = pd.DataFrame()
+
+# Copy the 'case_date' column from source_df to the 'CaseDate' column in target_df without any transformation
+target_df['CaseDate'] = source_df['case_date']
+
+# Concatenate the 'firstname' and 'lastname' columns from source_df (with a space in between) and store the result in the 'FullName' column in target_df
+target_df['FullName'] = source_df['firstname'] + " " + source_df['lastname']
+
+# Copy the 'case_type' column from source_df to the 'CaseType' column in target_df without any transformation
+target_df['CaseType'] = source_df['case_type']
+
+# Return the target_df as the output of the script
+target_df

transformed_source.csv

@@ -0,0 +1,101 @@
+,Date_of_Case,Fee,FullName,CaseNumber,CaseKind,Location,Weather
+0,2023/01/16,250,Jane Okafor,case--6190,Civil,BOSTO,snowy
+1,2023/08/10,100,Elena Malcolm,case--3092,Civil,CHIC,sunny
+2,2023/06/14,150,Alan Nasser,case--5947,Civil,BOSTO,rainy
+3,2023/07/17,250,Miguel Smith,case--7727,Family,LOSAN,snowy
+4,2023/07/25,150,John Kim,case--4120,Criminal,BOSTO,rainy
+5,2023/07/14,100,John Brown,case--8850,Civil,LOSAN,snowy
+6,2023/01/19,100,Dmitri Nasser,case--2308,Criminal,CHIC,sunny
+7,2023/02/26,100,Alan Rodriguez,case--4477,Criminal,CHIC,cloudy
+8,2023/02/10,200,Alice Brown,case--9490,Criminal,CHICA,snowy
+9,2023/09/12,100,Nadia Smith,case--4111,Family,LOSA,sunny
+10,2023/02/25,150,Chen Kim,case--9421,Criminal,BOST,sunny
+11,2023/09/15,200,John Kim,case--3270,Family,HOUST,sunny
+12,2023/07/22,200,Nadia Patel,case--1501,Family,HOUS,cloudy
+13,2023/01/27,150,Lakshmi Lee,case--8321,Family,HOUST,snowy
+14,2023/01/14,100,John Brown,case--2748,Family,LOSAN,snowy
+15,2023/07/13,100,Miguel Malcolm,case--3163,Family,LOSA,rainy
+16,2023/02/26,150,Alice Smith,case--4296,Civil,BOSTO,cloudy
+17,2023/09/25,200,Terrance Patel,case--2230,Criminal,HOUS,snowy
+18,2023/02/13,100,Alan Ivanov,case--9353,Family,NEWY,sunny
+19,2023/04/18,100,Alice Chatterjee,case--8786,Civil,CHICA,rainy
+20,2023/09/11,100,Jane Brown,case--6001,Criminal,LOSA,snowy
+21,2023/02/16,250,Jane Okafor,case--9434,Criminal,BOST,snowy
+22,2023/07/22,100,Dmitri Chatterjee,case--1042,Criminal,BOST,rainy
+23,2023/08/28,150,Miguel Smith,case--1427,Family,LOSA,cloudy
+24,2023/06/14,200,Miguel Johnson,case--7553,Civil,CHIC,sunny
+25,2023/02/24,250,Chen Ivanov,case--2242,Civil,LOSA,rainy
+26,2023/06/23,250,Terrance Rodriguez,case--6940,Criminal,HOUST,rainy
+27,2023/01/10,150,Elena Johnson,case--4064,Civil,HOUS,rainy
+28,2023/01/15,100,Chen Patel,case--3129,Civil,NEWY,rainy
+29,2023/08/16,150,Oluwaseun Malcolm,case--2758,Civil,BOSTO,snowy
+30,2023/02/24,250,Lakshmi Ivanov,case--9562,Criminal,BOSTO,sunny
+31,2023/05/15,250,Terrance Okafor,case--2292,Criminal,BOST,rainy
+32,2023/06/26,250,Jane Patel,case--7889,Criminal,LOSAN,cloudy
+33,2023/02/14,150,John Rodriguez,case--5178,Family,HOUS,sunny
+34,2023/05/15,150,Terrance Patel,case--5004,Civil,HOUST,snowy
+35,2023/03/19,200,Alice Johnson,case--2883,Family,NEWYO,snowy
+36,2023/02/12,200,Alan Rodriguez,case--4416,Family,BOSTO,rainy
+37,2023/07/25,200,Chen Malcolm,case--9332,Civil,HOUS,snowy
+38,2023/09/15,250,Miguel Chatterjee,case--7699,Civil,BOST,rainy
+39,2023/03/13,100,Nadia Lee,case--7258,Civil,NEWYO,snowy
+40,2023/05/27,200,Nadia Brown,case--7490,Civil,HOUS,snowy
+41,2023/02/22,100,Alice Johnson,case--8231,Civil,CHIC,cloudy
+42,2023/03/18,100,Nadia Malcolm,case--2720,Criminal,NEWY,cloudy
+43,2023/06/11,100,Nadia Brown,case--4277,Criminal,BOST,snowy
+44,2023/02/22,100,Oluwaseun Okafor,case--9738,Criminal,NEWYO,snowy
+45,2023/08/19,250,Jane Patel,case--2452,Civil,BOSTO,snowy
+46,2023/09/27,100,Alan Lee,case--1899,Family,NEWY,rainy
+47,2023/04/21,150,Dmitri Malcolm,case--8404,Family,LOSAN,rainy
+48,2023/03/10,100,Alice Chatterjee,case--4240,Family,LOSA,snowy
+49,2023/05/13,250,Elena Kim,case--6153,Family,CHIC,rainy
+50,2023/09/10,200,Alan Patel,case--3485,Criminal,CHIC,cloudy
+51,2023/08/18,200,Lakshmi Kim,case--5520,Criminal,LOSAN,sunny
+52,2023/02/21,250,Alan Patel,case--9879,Criminal,LOSA,sunny
+53,2023/05/12,200,Jane Brown,case--5259,Criminal,NEWYO,rainy
+54,2023/01/20,100,Oluwaseun Patel,case--8333,Criminal,BOSTO,cloudy
+55,2023/01/23,200,Chen Nasser,case--2711,Civil,LOSAN,sunny
+56,2023/03/12,100,Miguel Brown,case--5100,Family,LOSAN,sunny
+57,2023/01/15,100,Terrance Rodriguez,case--4849,Criminal,LOSAN,rainy
+58,2023/05/17,150,Jane Lee,case--8058,Criminal,NEWY,cloudy
+59,2023/04/18,100,Chen Okafor,case--9076,Civil,NEWYO,sunny
+60,2023/02/22,200,Lakshmi Chatterjee,case--5230,Criminal,BOST,rainy
+61,2023/08/18,200,John Brown,case--7094,Criminal,LOSA,cloudy
+62,2023/08/17,150,Oluwaseun Lee,case--8915,Civil,BOSTO,sunny
+63,2023/08/18,100,Alan Malcolm,case--9030,Family,CHIC,sunny
+64,2023/02/13,150,Chen Malcolm,case--1482,Criminal,HOUS,cloudy
+65,2023/02/16,100,John Brown,case--3535,Criminal,BOST,rainy
+66,2023/08/20,250,Chen Johnson,case--2029,Criminal,HOUST,sunny
+67,2023/01/10,250,Alan Kim,case--1812,Civil,HOUST,sunny
+68,2023/02/18,150,Alice Chatterjee,case--5295,Civil,CHICA,snowy
+69,2023/08/25,150,Miguel Lee,case--6850,Criminal,LOSA,sunny
+70,2023/05/12,150,Alan Malcolm,case--7973,Criminal,BOST,cloudy
+71,2023/05/19,200,Chen Johnson,case--5221,Family,HOUS,snowy
+72,2023/06/17,250,John Okafor,case--4117,Criminal,BOSTO,sunny
+73,2023/03/18,100,Elena Patel,case--2368,Family,HOUST,rainy
+74,2023/06/22,200,Lakshmi Rodriguez,case--8384,Family,NEWY,cloudy
+75,2023/07/14,100,Miguel Smith,case--4476,Civil,NEWYO,cloudy
+76,2023/03/26,100,Chen Brown,case--4545,Civil,HOUST,snowy
+77,2023/06/22,250,Dmitri Chatterjee,case--4421,Civil,HOUS,snowy
+78,2023/03/20,150,Miguel Patel,case--6559,Criminal,NEWYO,snowy
+79,2023/07/11,250,Oluwaseun Kim,case--1803,Civil,BOSTO,sunny
+80,2023/03/13,150,Elena Okafor,case--8622,Civil,NEWYO,cloudy
+81,2023/05/27,200,Alice Lee,case--9488,Criminal,LOSAN,cloudy
+82,2023/05/14,150,Alice Patel,case--4581,Civil,CHICA,sunny
+83,2023/06/27,250,Terrance Malcolm,case--2388,Criminal,CHIC,sunny
+84,2023/02/13,100,Terrance Ivanov,case--6529,Criminal,LOSAN,snowy
+85,2023/01/21,150,Terrance Patel,case--4443,Family,BOST,sunny
+86,2023/09/22,200,John Malcolm,case--8721,Civil,NEWYO,snowy
+87,2023/02/12,250,Miguel Malcolm,case--3780,Family,NEWYO,cloudy
+88,2023/04/26,250,Alan Kim,case--2663,Criminal,NEWY,rainy
+89,2023/03/16,150,Lakshmi Ivanov,case--8702,Criminal,LOSA,snowy
+90,2023/07/22,100,Jane Ivanov,case--1232,Criminal,BOSTO,rainy
+91,2023/05/28,150,Nadia Okafor,case--5215,Family,HOUS,cloudy
+92,2023/03/14,250,Oluwaseun Okafor,case--6631,Criminal,BOST,rainy
+93,2023/06/17,150,Alan Nasser,case--1405,Civil,BOST,snowy
+94,2023/08/13,100,Oluwaseun Kim,case--8816,Civil,LOSAN,cloudy
+95,2023/07/20,150,Oluwaseun Brown,case--2665,Family,NEWYO,sunny
+96,2023/05/16,100,Alan Patel,case--2874,Family,NEWYO,sunny
+97,2023/07/15,100,Nadia Chatterjee,case--2037,Family,HOUST,rainy
+98,2023/04/18,200,Dmitri Johnson,case--5402,Criminal,HOUS,snowy
+99,2023/08/14,250,Chen Johnson,case--3569,Civil,BOST,sunny