Gemini 3 Flash agentic vision - Section 6: Multi-Step Extraction and Plotting (full output)

section6_output.txt

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AGENTIC VISION - MULTI-STEP EXTRACTION
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[THINK] **My Approach to Data Extraction and Analysis from the Image**

Okay, so I'm looking at this image, and the goal is to pull some data from a table, specifically the first 10 rows. There are several tables in this image, so let's start by clarifying what we're dealing with. The image contains tax tables, and it looks like there are a few options. I see a "Single or Married Filing Separately" table, and a "Head of Household" table. I'll focus on one, for simplicity and clarity. The one at the top 
[CODE]
import PIL.Image
import PIL.ImageDraw

# Load the image to get its dimensions
img = PIL.Image.open('input_file_0.jpeg')
width, height = img.size

# Define regions of interest to find the table
# The table is roughly in the top half of the image.
# Let's zoom in on the "Single or Married Filing Separately" table.
# It starts from roughly (340, 160) to (1000, 600) in normalized coordinates.
# Let's do a wider crop first to identify headers and rows better.

objs = [
    {'box_2d': [0, 200, 600, 800], 'label': 'top_table_area'},
    {'box_2d': [160, 360, 480, 850], 'label': 'single_filing_table'},
    {'box_2d': [430, 70, 720, 560], 'label': 'head_of_household_table'}
]

# Just crop the Single filing table for now as it's very clear.
# Let's zoom in more to read values.
# The table headers are around y=180-350.
# Rows are on the left.

def get_crop(box, img_w, img_h):
    ymin, xmin, ymax, xmax = box
    return img.crop((xmin * img_w / 1000, ymin * img_h / 1000, xmax * img_w / 1000, ymax * img_h / 1000))

crop1 = get_crop([160, 360, 480, 850], width, height)
crop1.save('single_filing_table.png')

# Also crop the headers and first few rows specifically
crop2 = get_crop([160, 360, 350, 850], width, height) # Headers and top rows
crop2.save('table_zoom.png')

print(f'Image size: {width}x{height}')

[RESULT] (Outcome.OUTCOME_OK)
Image size: 2250x1500

[IMAGE] image/png, 571,849 bytes
[IMAGE] image/png, 929,077 bytes
[THINK] **Analyzing the "Single or Married Filing Separately" Table**

Okay, so I'm looking at this crop, `single_filing_table.png`, and it's clear this is a portion of the "Single or Married Filing Separately" table. My initial thought is to identify the structure, the rows and columns. I see the column headers immediately: "$30,000 - 39,999", "$40,000 - 49,999", and so on. I need to figure out the rows, which represent the "Higher Paying Job Annual Taxable Wage & Salary."

Right, I need to find the fu
[CODE]
# Crop the left side and top of the first table to get headers
crop_top_left = get_crop([100, 50, 500, 400], width, height)
crop_top_left.save('table_top_left.png')

# The "Single or Married Filing Separately" table columns start at some point.
# Let's see the headers for that section.
# The table is roughly from row $0 to something.
# Let's get the headers for the columns as well.
crop_cols = get_crop([100, 250, 250, 850], width, height)
crop_cols.save('table_cols.png')

[IMAGE] image/png, 554,079 bytes
[IMAGE] image/png, 736,870 bytes
[THINK] **Analyzing the Tax Table: Decoding the "Single or Married Filing Separately" Section**

Okay, so I'm looking at this tax table, specifically the section labeled "Single or Married Filing Separately." My initial goal is to extract some data, and it looks like the user wants data from the first 10 rows and a few columns. I'll need to figure out the table's structure. First, I noted that the "Single or Married Filing Separately" label acts as a header for multiple columns, defining a specific sect
[CODE]
# Zoom into the first 10 rows and first 4 columns of the Single table
crop_data = get_crop([160, 250, 400, 550], width, height)
crop_data.save('data_top_rows.png')

[IMAGE] image/png, 475,103 bytes
[THINK] **Data Extraction and Visualization Plan**

Okay, so I need to extract data from what looks like a table representing some kind of financial or statistical information. The prompt specifies "the first 10 rows," which is straightforward enough, but I need to make sure I'm grabbing the right headers and data. First, I'll identify the column headers. The images are a bit fragmented, but I can see the column headers "$0 - 9,999", "$10,000 - 19,999", and so on.

Next, I need the corresponding row lab
[CODE]
import matplotlib.pyplot as plt
import pandas as pd

data = [
    {"Higher Paying Job Wage": "$0 - 9,999", "$0 - 9,999": 0, "$10,000 - 19,999": 940, "$20,000 - 29,999": 1020, "$30,000 - 39,999": 1020},
    {"Higher Paying Job Wage": "$10,000 - 19,999", "$0 - 9,999": 940, "$10,000 - 19,999": 1540, "$20,000 - 29,999": 1620, "$30,000 - 39,999": 2020},
    {"Higher Paying Job Wage": "$20,000 - 29,999", "$0 - 9,999": 1020, "$10,000 - 19,999": 1620, "$20,000 - 29,999": 2100, "$30,000 - 39,999": 3100},
    {"Higher Paying Job Wage": "$30,000 - 39,999", "$0 - 9,999": 1020, "$10,000 - 19,999": 2020, "$20,000 - 29,999": 3100, "$30,000 - 39,999": 4100},
    {"Higher Paying Job Wage": "$40,000 - 59,999", "$0 - 9,999": 1870, "$10,000 - 19,999": 3470, "$20,000 - 29,999": 4550, "$30,000 - 39,999": 5550},
    {"Higher Paying Job Wage": "$60,000 - 79,999", "$0 - 9,999": 1870, "$10,000 - 19,999": 3470, "$20,000 - 29,999": 4690, "$30,000 - 39,999": 5890},
    {"Higher Paying Job Wage": "$80,000 - 99,999", "$0 - 9,999": 2000, "$10,000 - 19,999": 3810, "$20,000 - 29,999": 5090, "$30,000 - 39,999": 6290},
    {"Higher Paying Job Wage": "$100,000 - 124,999", "$0 - 9,999": 2040, "$10,000 - 19,999": 3840, "$20,000 - 29,999": 5120, "$30,000 - 39,999": 6320},
    {"Higher Paying Job Wage": "$125,000 - 149,999", "$0 - 9,999": 2040, "$10,000 - 19,999": 3840, "$20,000 - 29,999": 5120, "$30,000 - 39,999": 6910},
    {"Higher Paying Job Wage": "$150,000 - 174,999", "$0 - 9,999": 2220, "$10,000 - 19,999": 4830, "$20,000 - 29,999": 6910, "$30,000 - 39,999": 8910}
]

df = pd.DataFrame(data)

# Print as a structured format (table)
print(df.to_string())

# Create a bar chart
df.set_index("Higher Paying Job Wage").plot(kind='bar', figsize=(12, 6))
plt.title('Tax Table: Single or Married Filing Separately')
plt.ylabel('Tax Amount')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.savefig('tax_bar_chart.png')

# Highest and lowest
# Sum of each row to compare? Or just absolute highest/lowest in the data?
# Let's say which row has the maximum and minimum total tax amount among these columns.
df['Total'] = df.iloc[:, 1:].sum(axis=1)
highest_row = df.loc[df['Total'].idxmax(), 'Higher Paying Job Wage']
lowest_row = df.loc[df['Total'].idxmin(), 'Higher Paying Job Wage']
print(f"Row with highest total values in these columns: {highest_row}")
print(f"Row with lowest total values in these columns: {lowest_row}")

[RESULT] (Outcome.OUTCOME_OK)
  Higher Paying Job Wage  $0 - 9,999  $10,000 - 19,999  $20,000 - 29,999  $30,000 - 39,999
0             $0 - 9,999           0               940              1020              1020
1       $10,000 - 19,999         940              1540              1620              2020
2       $20,000 - 29,999        1020              1620              2100              3100
3       $30,000 - 39,999        1020              2020              3100              4100
4       $40,000 - 59,999        1870              3470              4550              5550
5       $60,000 - 79,999        1870              3470              4690              5890
6       $80,000 - 99,999        2000              3810              5090              6290
7     $100,000 - 124,999        2040              3840              5120              6320
8     $125,000 - 149,999        2040              3840              5120              6910
9     $150,000 - 174,999        2220              4830              6910              8910
Row with highest total values in these columns: $150,000 - 174,999
Row with lowest total values in these columns: $0 - 9,999

[IMAGE] image/png, 52,545 bytes
[THINK] **My Analysis of the Data**

Okay, so I've pulled the data. I have the first 10 rows and 4 columns from that "Single or Married Filing Separately" table. The column headers are clear – thankfully, that's one less thing to worry about. I've already generated the bar chart, so that's done. I've identified the rows with the maximum and minimum total values, and now it's time to craft the final response.

Before I finalize everything, I should double-check those headers to ensure accuracy, and make 
[ANSWER] The table extracted from the image is the **"Single or Married Filing Separately"** tax table, which calculates additional tax based on the annual taxable wage and salary of both a higher-paying job (rows) and a lower-paying job (columns).

### Extracted Data (First 10 Rows, First 4 Columns)

| Higher Paying Job Wage | $0 - 9,999 | $10,000 - 19,999 | $20,000 - 29,999 | $30,000 - 39,999 |
| :--- | :--- | :--- | :--- | :--- |
| **$0 - 9,999** | $0 | $940 | $1,020 | $1,020 |
| **$10,000 - 19,999** | $940 | $1,540 | $1,620 | $2,020 |
| **$20,000 - 29,999** | $1,020 | $1,620 | $2,100 | $3,100 |
| **$30,000 - 39,999** | $1,020 | $2,020 | $3,100 | $4,100 |
| **$40,000 - 59,999** | $1,870 | $3,470 | $4,550 | $5,550 |
| **$60,000 - 79,999** | $1,870 | $3,470 | $4,690 | $5,890 |
| **$80,000 - 99,999** | $2,000 | $3,810 | $5,090 | $6,290 |
| **$100,000 - 124,999** | $2,040 | $3,840 | $5,120 | $6,320 |
| **$125,000 - 149,999** | $2,040 | $3,840 | $5,120 | $6,910 |
| **$150,000 - 174,999** | $2,220 | $4,830 | $6,910 | $8,910 |

### Summary of Values
*   **Highest Value:** The row for the **$150,000 - 174,999** wage bracket contains the highest values in this subset, with a peak of **$8,910** at the intersection with the $30,000 - 39,999 lower-paying job column.
*   **Lowest Value:** The row for the **$0 - 9,999** wage bracket contains the lowest values, starting at **$0** for the lowest combined income category.

A bar chart comparing these values across the 10 rows and 4 columns has been generated and is displayed below.

![Tax Bar Chart](tax_bar_chart.png)

Saving generated images...
  Saved: section6_chart_1.png
  Saved: section6_chart_2.png
  Saved: section6_chart_3.png
  Saved: section6_chart_4.png
  Saved: section6_chart_5.png
  Saved: section6_chart_6.png
Done!