app.py

# -*- coding: utf-8 -*-

import torch
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
import zipfile
import os
import gradio as gr
from PIL import Image

CHARS = "~=" + " abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789,.'-!?:;\""
BLANK = 0
PAD = 1
CHARS_DICT = {c: i for i, c in enumerate(CHARS)}
TEXTLEN = 30

tokens_list = list(CHARS_DICT.keys())
silence_token = '|'

if silence_token not in tokens_list:
    tokens_list.append(silence_token)


def fit_picture(img):
    target_height = 32
    target_width = 400

    # Calculate resize dimensions
    aspect_ratio = img.width / img.height
    if aspect_ratio > (target_width / target_height):
        resize_width = target_width
        resize_height = int(target_width / aspect_ratio)
    else:
        resize_height = target_height
        resize_width = int(target_height * aspect_ratio)

    # Resize transformation
    resize_transform = torchvision.transforms.Resize((resize_height, resize_width))

    # Pad transformation
    padding_height = (target_height - resize_height) if target_height > resize_height else 0
    padding_width = (target_width - resize_width) if target_width > resize_width else 0
    pad_transform = torchvision.transforms.Pad((0, 0, padding_width, padding_height), fill=0, padding_mode='constant')

    transformss = torchvision.transforms.Compose([
        torchvision.transforms.Grayscale(num_output_channels = 1),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(0.5,0.5),
        resize_transform,
        pad_transform
    ])

    fin_img = transformss(img)
    return fin_img

def load_model(filename):
    data = torch.load(filename, map_location=torch.device('cpu'), weights_only=True)
    recognizer.load_state_dict(data["recognizer"])
    optimizer.load_state_dict(data["optimizer"])

def ctc_decode_sequence(seq):
    """Removes blanks and repetitions from the sequence."""
    ret = []
    prev = BLANK
    for x in seq:
        if prev != BLANK and prev != x:
            ret.append(prev)
        prev = x
    if seq[-1] == 66:
        ret.append(66)
    return ret

def ctc_decode(codes):
    """Decode a batch of sequences."""
    ret = []
    for cs in codes.T:
        ret.append(ctc_decode_sequence(cs))
    return ret


def decode_text(codes):
  chars = [CHARS[c] for c in codes]
  return ''.join(chars)

class Residual(torch.nn.Module):
    def __init__(self, in_channels, out_channels, stride, pdrop = 0.2):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, 3, stride, 1)
        self.bn1 = torch.nn.BatchNorm2d(out_channels)
        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, 3, 1, 1)
        self.bn2 = torch.nn.BatchNorm2d(out_channels)
        if in_channels != out_channels or stride != 1:
          self.skip = torch.nn.Conv2d(in_channels, out_channels, 1, stride, 0)
        else:
          self.skip = torch.nn.Identity()
        self.dropout = torch.nn.Dropout2d(pdrop)

    def forward(self, x):
        y = torch.nn.functional.relu(self.bn1(self.conv1(x)))
        y = torch.nn.functional.relu(self.bn2(self.conv2(y)) + self.skip(x))
        y = self.dropout(y)
        return y

class TextRecognizer(torch.nn.Module):
    def __init__(self, labels):
        super().__init__()
        self.feature_extractor = torch.nn.Sequential(
            Residual(1, 32, 1),
            Residual(32, 32, 2),
            Residual(32, 32, 1),
            Residual(32, 64, 2),
            Residual(64, 64, 1),
            Residual(64, 128, (2,1)),
            Residual(128, 128, 1),
            Residual(128, 128, (2,1)),
            Residual(128, 128, (2,1)),
        )
        self.recurrent = torch.nn.LSTM(128, 128, 1 ,bidirectional = True)
        self.output = torch.nn.Linear(256, labels)

    def forward(self, x):
        x = self.feature_extractor(x)
        x = x.squeeze(2)
        x = x.permute(2,0,1)
        x,_ = self.recurrent(x)
        x = self.output(x)
        return x

recognizer = TextRecognizer(len(CHARS))
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print("Device:", DEVICE)
LR = 1e-3

recognizer.to(DEVICE)
optimizer = torch.optim.Adam(recognizer.parameters(), lr=LR)

load_model('model.pt')
recognizer.eval()

def ctc_read(image):
    imagefin = fit_picture(image)
    image_tensor = imagefin.unsqueeze(0).to(DEVICE)
    print(image_tensor.size())

    with torch.no_grad():
        scores = recognizer(image_tensor)

    predictions = scores.argmax(2).cpu().numpy()

    decoded_sequences = ctc_decode(predictions)

    # Convert decoded sequences to text
    for i in decoded_sequences:
        decoded_text = decode_text(i)

    return decoded_text


# Gradio Interface
iface = gr.Interface(
    fn=ctc_read,
    inputs=gr.Image(type="pil"),  # PIL Image input
    outputs="text",  # Text output
    title="Handwritten Text Recognition",
    description="Upload an image, and the custome AI will extract the text."
)

iface.launch(share=True)