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import openpyxl
from PIL import Image, ImageDraw, ImageFont
import base64
import requests
import gradio as gr
import os
# OpenAI API Key
api_key = os.environ.get("api_key")
# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def take_screenshot_of_cells(file_path, sheet_name, start_cell, end_cell, query):
# Load the workbook and the specified sheet
workbook = openpyxl.load_workbook(file_path)
sheet = workbook[sheet_name]
# Get the coordinates of the start and end cells
start_col, start_row = openpyxl.utils.cell.coordinate_to_tuple(start_cell)
end_col, end_row = openpyxl.utils.cell.coordinate_to_tuple(end_cell)
# Calculate the number of rows and columns
num_cols = end_col - start_col + 1
num_rows = end_row - start_row + 1
# Set the width and height of each cell (you can adjust these values as needed)
cell_width = 100
cell_height = 30
# Create a blank image with the calculated size
image_width = cell_width * num_cols
image_height = cell_height * num_rows
image = Image.new('RGB', (image_width, image_height), 'white')
draw = ImageDraw.Draw(image)
# Load a font (you may need to specify the path to a font file on your system)
font = ImageFont.load_default()
# Draw the cells and their content
for row in range(num_rows):
for col in range(num_cols):
cell_value = sheet.cell(row=start_row + row, column=start_col + col).value
x0 = col * cell_width
y0 = row * cell_height
x1 = x0 + cell_width
y1 = y0 + cell_height
# Draw the cell border
draw.rectangle([x0, y0, x1, y1], outline='black')
# Draw the cell value
if cell_value is not None:
draw.text((x0 + 5, y0 + 5), str(cell_value), fill='black', font=font)
# Save the image
output_image_path = 'screenshot.png'
image.save(output_image_path)
# Encode the image to base64
base64_image = encode_image(output_image_path)
# Send the image to the OpenAI API
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
payload = {
"model": "gpt-4o", # Assuming the model name is 'gpt-4-vision'
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": query
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
response=response.json()
return response['choices'][0]['message']['content']
def gradio_interface(excel_file, sheet_name, start_cell, end_cell, query):
return take_screenshot_of_cells(excel_file.name, sheet_name, start_cell, end_cell, query)
# Create Gradio Interface
inputs = [
gr.File(label="Upload Excel File"),
gr.Textbox(label="Sheet Name"),
gr.Textbox(label="Start Cell"),
gr.Textbox(label="End Cell"),
gr.Textbox(label="Query")
]
outputs = gr.Textbox(label="Output")
gr.Interface(fn=gradio_interface, inputs=inputs, outputs=outputs, title="Excel Screenshot Analyzer").launch()
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