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import gradio as gr
import base64
from openai import OpenAI
from pathlib import Path
import tempfile
import os
API_KEY = os.getenv("openai")
def process_image(image_path):
client = OpenAI(api_key=API_KEY)
# Read the image file and encode to base64
with open(image_path, "rb") as image_file:
encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
# Use GPT-4 Vision to perform OCR
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "system",
"content": "You are an OCR system. Extract all text from the image and return it without any additional commentary."
},
{
"role": "user",
"content": [
{"type": "text", "text": "What text is in this image?"},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encoded_image}"
}
}
]
}
],
max_tokens=300
)
extracted_text = response.choices[0].message.content
# Format text for dyslexia-friendly reading
formatted_text = f"<p style='font-family: Arial, sans-serif; font-size: 18px; line-height: 1.5; font-weight: bold;'>{extracted_text}</p>"
# Generate speech from text
speech_file_path = Path(tempfile.gettempdir()) / "speech.mp3"
speech_response = client.audio.speech.create(
model="tts-1",
voice="nova",
input=extracted_text
)
speech_response.stream_to_file(speech_file_path)
return formatted_text, str(speech_file_path)
# Gradio interface
iface = gr.Interface(
fn=process_image,
inputs=[
gr.Image(type="filepath", label="Upload Image")
],
outputs=[
gr.HTML(label="Extracted and Formatted Text"),
gr.Audio(label="Text-to-Speech")
],
title="Dyslexia-Friendly Reading Assistant",
description="Upload an image of text. The app will extract the text, format it for easier reading, and provide an audio version."
)
iface.launch() |