app.py

# app.py

import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont
from model import load_model
from utils import preprocess_image, decode_predictions
import os

# Load the model (ensure the path is correct)
MODEL_PATH = "finetuned_recog_model.pth"
FONT_PATH = "NotoSansEthiopic-Regular.ttf"  # Update the path to your font

# Check if model file exists
if not os.path.exists(MODEL_PATH):
    raise FileNotFoundError(f"Model file not found at {MODEL_PATH}. Please provide the correct path.")

# Check if font file exists
if not os.path.exists(FONT_PATH):
    raise FileNotFoundError(f"Font file not found at {FONT_PATH}. Please provide the correct path.")

# Load the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = load_model(MODEL_PATH, device=device)

# Load the font for rendering Amharic text
from matplotlib import font_manager as fm
import matplotlib.pyplot as plt

ethiopic_font = fm.FontProperties(fname=FONT_PATH, size=15)
pil_font = ImageFont.truetype(FONT_PATH, size=20)

def recognize_text(image: Image.Image) -> str:
    """
    Function to recognize text from an image.
    """
    # Preprocess the image
    input_tensor = preprocess_image(image).unsqueeze(0).to(device)  # [1, 3, 224, 224]
    
    # Perform inference
    with torch.no_grad():
        log_probs = model(input_tensor)  # [H*W, 1, vocab_size]
    
    # Decode predictions
    recognized_texts = decode_predictions(log_probs)
    
    return recognized_texts[0]

def recognize_and_overlay(image: Image.Image) -> Image.Image:
    """
    Function to recognize text and overlay it on the image.
    """
    recognized_text = recognize_text(image)
    
    # Overlay text on the image
    draw = ImageDraw.Draw(image)
    text_position = (10, 10)  # Top-left corner
    text_color = (255, 0, 0)  # Red color
    draw.text(text_position, f"Recognized: {recognized_text}", font=pil_font, fill=text_color)
    
    return image

# Define Gradio Interface
iface = gr.Interface(
    fn=recognize_and_overlay,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Image(type="pil", label="Image with Recognized Text"),
    title="Amharic Text Recognition",
    description="Upload an image containing Amharic text. The app will recognize and overlay the text on the image."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()