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import os
import logging
import json
from fastapi import FastAPI, UploadFile
from fastapi.responses import FileResponse
import gradio as gr
from PIL import Image
import PIL
import numpy as np
import pypdfium2 as pdfium
from ultralytics import YOLO
from ultralytics.engine.results import Results, Masks
import uvicorn
import cv2
import uuid
from openai import OpenAI
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
PROMPT = """
You are analyzing the spec sheet of a solar panel. If there is no text after the line \"PDF Extract Text Contents Below:\" report that there is no spec data provided as dictionary with a field called 'error'.
If there is text, please answer the following questions, format them as a JSON dictionary. Include the units of dimensions, weight, and cable lengths.\n
"""
# from solareyes.sam import SAM
client = OpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_API_KEY"),
)
app = FastAPI()
# Directories
image_dir = './pdf_images/'
cropped_dir = './output/'
pdf_dir = './pdf_downloads/'
os.makedirs(image_dir, exist_ok=True)
os.makedirs(cropped_dir, exist_ok=True)
os.makedirs(pdf_dir, exist_ok=True)
def parse_pdf_text(file):
pdf = pdfium.PdfDocument(file)
all_text = "PDF Extract Text Contents Below: \n\n"
for page in pdf:
textpage = page.get_textpage()
text_all = textpage.get_text_bounded()
all_text += text_all
logger.debug(f"Text: {all_text}")
#use openai to ask questions about text
q1 = "What are module dimensions in L x W x H? Result key should be \"module_dimensions\""
q2 = "What is the module weight in kilograms? Result key should be \"module_weight\""
q3 = "What are the cable lengths in millimeters? Result key should be \"cable_length\""
q4 = "What brand, name, or model are the connectors? Result key should be \"connector\""
q5 = "How many pieces per container? Prefer 40' HQ or HC, if not available try 53' Result key should be \"pieces_per_container\""
q6 = "What is the model number? Result key should be \"model_number\""
question = PROMPT + q1 + "\n" + q2 + "\n" + q3 + "\n" + q4 + "\n" + q5 + "\n" + q6 + "\n" + all_text
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": question,
}
],
model="gpt-3.5-turbo",
response_format={ "type": "json_object"}
)
return chat_completion.choices[0].message.content
def segment_solar_panel(image) -> Results:
# Perform inference
seg_model: YOLO = YOLO('model/autodistill_best_seg.pt')
results: Results = seg_model.predict(image, imgsz=(841, 595), retina_masks=True)
return results
def resize_and_pad(subject_image: Image.Image):
# Resize subject image to 80% of 1200px while maintaining aspect ratio
target_height = int(1200 * 0.8)
aspect_ratio = subject_image.width / subject_image.height
new_width = int(target_height * aspect_ratio)
resized_subject = subject_image.resize((new_width, target_height), Image.LANCZOS)
# Create a new transparent image
new_image = Image.new("RGBA", (1200, 1200), (0, 0, 0, 0))
# Calculate the position to paste the resized subject image
x = (1200 - new_width) // 2
y = (1200 - target_height) // 2
# Paste the resized subject image onto the transparent image
new_image.paste(resized_subject, (x, y), resized_subject)
# Save or return the PNG image
png_image = new_image
# Create a new image with a white background
jpg_image = Image.new("RGB", (1200, 1200), (255, 255, 255))
jpg_image.paste(png_image, (0, 0), png_image)
# Save or return the JPEG image
return png_image, jpg_image
def segment_image_core(img: np.ndarray | Image.Image) -> Image.Image:
if type(img) is np.ndarray:
img = Image.fromarray(img)
results = segment_solar_panel(img)
sections = []
for i, result in enumerate(results):
print(f"Result {i}")
result: Results
try:
h2, w2, c2 = result.orig_img.shape
# Deal with boxes
i = 0
for box in result.boxes:
x1, y1, x2, y2 = box.xyxy[0].tolist()
sections.append(((int(x1), int(y1), int(x2), int(y2)), f"{section_labels[0]} Bounding Box - index {i} - conf {box.conf}"))
# Now the masks
masks: Masks = result.masks
try:
mask = masks[i]
cpu_mask = mask.cpu()
squeezed_mask = cpu_mask.data.numpy()
transposed_mask = squeezed_mask.transpose(1, 2, 0)
kernel = cv2.getStructuringElement(cv2.MORPH_OPEN, (11, 11))
opened_mask = cv2.morphologyEx(transposed_mask, cv2.MORPH_OPEN, kernel, iterations=3)
cv_mask = cv2.resize(opened_mask, (w2, h2))
image_mask = Image.fromarray((cv_mask * 255).astype(np.uint8)).filter(PIL.ImageFilter.GaussianBlur(1))
img_out = img.copy()
img_out.putalpha(image_mask)
img_out = img_out.crop((x1, y1, x2, y2))
png_img, jpg_img = resize_and_pad(img_out)
sections.append((cv_mask, f"{section_labels[0]} Mask - Index: {i}"))
except TypeError as e:
print(f"Error processing image: {e}, probably no masks.")
i += 1
except IndexError as e:
print(f"Error processing image: {e}, probably no boxes.")
return (img, sections), jpg_img
def pdf_to_image(pdf, end = None, start = 0) -> list[Image.Image]:
pdf = pdfium.PdfDocument(pdf)
page_images = []
if end is None:
end = len(pdf) # get the number of pages in the document
for i in range(start, end):
page = pdf[i]
page_image = page.render(scale=4).to_pil()
page_images.append(page_image)
return page_images
def pdf_first_page_to_image(pdf) -> Image.Image:
return pdf_to_image(pdf, 1, 0)[0]
with gr.Blocks() as demo:
section_labels = ['Solar Panel']
def segment_image(img):
img_sections, jpg_img = segment_image_core(img)
return img_sections
def process_pdf(pdf):
image = pdf_first_page_to_image(pdf)
return segment_image(image)
pdf_input = gr.File(label="Upload PDF", file_types=['pdf'], height=100)
pdf_image = gr.Gallery(label="PDF Page Images")
pdf_to_image_btn = gr.Button("Convert PDF to Image")
with gr.Row():
img_output_pdf = gr.AnnotatedImage(label="Extracted product image", height=400)
pdf_extract_btn = gr.Button("Identify Solar Panel from PDF")
with gr.Row():
text_input = gr.Textbox(label="Enter Text", placeholder=PROMPT)
text_output = gr.Textbox(label="Output", placeholder="Spec analysis will appear here")
pdf_text_btn = gr.Button("Extract specs from PDF Text")
gr.Examples(
inputs = pdf_input,
examples = [os.path.join(pdf_dir, file) for file in os.listdir(pdf_dir)],
)
pdf_extract_btn.click(process_pdf, [pdf_input], img_output_pdf)
pdf_text_btn.click(parse_pdf_text, [pdf_input], text_output)
pdf_to_image_btn.click(pdf_to_image, [pdf_input], pdf_image)
#Accept a PDF file, return a jpeg image
@app.post("/uploadPdf", response_class=FileResponse)
def extract_image(uploadFile: UploadFile) -> FileResponse:
file = uploadFile.file.read()
image = pdf_first_page_to_image(file)
img_segments, jpeg_image = segment_image_core(image)
id = str(uuid.uuid4())
filename = f"{cropped_dir}/cropped_{id}.jpg"
jpeg_image.save(filename)
return FileResponse(filename)
#Accept a PDF file, return a text summary
@app.post("/parsePdf")
def parse_info(uploadFile: UploadFile):
logger.info(f"Receiving file {uploadFile.filename}")
file = uploadFile.file.read()
logger.info(f"Received file {uploadFile.filename}")
answer = parse_pdf_text(file)
return {"answer": json.loads(answer)}
app = gr.mount_gradio_app(app, demo, path="/")
if __name__ == "__main__":
# app = gr.mount_gradio_app(app, demo, path="/gradio")
uvicorn.run(app, port=7860)
# demo.launch(share=True)
# demo.launch(share=True, auth=(os.environ.get("GRADIO_USERNAME"), os.environ.get("GRADIO_PASSWORD")))