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import os
import streamlit as st
from PIL import Image
import numpy as np
from transformers import AutoProcessor, AutoModelForPreTraining
import torch
from huggingface_hub import login
# Get the token from environment variables (set in Hugging Face Space)
token = os.getenv("HF_Token")
login(token)
# Initialize the Hugging Face model
processor = AutoProcessor.from_pretrained("google/paligemma-3b-mix-224")
model = AutoModelForPreTraining.from_pretrained("google/paligemma-3b-mix-224")
# Function to transcribe handwritten notes using Hugging Face model
def transcribe_handwriting(image):
# Convert image to RGB and numpy array
image = image.convert("RGB")
image_array = np.array(image)
# Prepare input for the model
inputs = processor(image_array, return_tensors="pt")
# Generate output
with torch.no_grad():
outputs = model.generate(**inputs, max_length=512)
transcription = processor.decode(outputs[0], skip_special_tokens=True)
return transcription
# Set Streamlit page configuration
st.set_page_config(
page_title="AI and ML Handwriting Reader",
page_icon="💊",
layout="centered",
)
# Function to display extracted text in a fancy div
def display_extracted_text(extracted_text):
# Custom HTML and CSS for styling
fancy_div = f"""
<div style="
border: 2px solid #128c7E;
border-radius: 10px;
padding: 20px;
background-color: #f0f0f0;
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
font-family: 'Arial', sans-serif;
color: #333;">
<h2 style="color: #128c7E;">Transcribed Text from Image:</h2>
<p style="font-size: 16px;">{extracted_text}</p>
</div>
"""
st.markdown(fancy_div, unsafe_allow_html=True)
# Header
st.title("AI and ML based Handwriting Reader")
# Upload prescription image
uploaded_file = st.file_uploader("Upload Handwriting Image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Open the image using PIL
image = Image.open(uploaded_file)
# Display uploaded image
st.image(image, caption="Uploaded Handwriting Image", width=400)
with st.spinner("Transcribing handwriting..."):
# Transcribe handwritten notes
extracted_text = transcribe_handwriting(image)
display_extracted_text(extracted_text)
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