app.py

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")))