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import streamlit as st
from transformers import pipeline
import pytesseract
import cv2
import numpy as np
from PIL import Image
# Load the model
model = pipeline("text2text-generation", model="google/paligemma-3b-mix-224")
# Function to extract text from image using OCR
def extract_text_from_image(image_file):
image = Image.open(image_file)
img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
text = pytesseract.image_to_string(img_cv)
return text
# Function to get AI interpretation of the prescription
def interpret_prescription(text):
response = model(text)
return response[0]['generated_text'].strip()
# Set Streamlit page configuration
st.set_page_config(
page_title="Prescription Reader",
page_icon="💊",
layout="centered",
)
# Header
st.title("Doctor's Prescription Reader 💊")
# Upload prescription image
uploaded_file = st.file_uploader("Upload Prescription Image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Display uploaded image
st.image(uploaded_file, caption="Uploaded Prescription", use_column_width=True)
with st.spinner("Extracting text from prescription..."):
# Extract text from image using OCR
extracted_text = extract_text_from_image(uploaded_file)
st.subheader("Extracted Text from Prescription:")
st.text(extracted_text)
if extracted_text:
# Interpret extracted text using the model
with st.spinner("Interpreting the prescription..."):
ai_response = interpret_prescription(extracted_text)
st.subheader("AI Interpretation:")
st.text(ai_response)
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