demo.py

import os
import gradio as gr
import requests
import json
import io
from gradio.components import Image
from PIL import Image as PILImage, ImageDraw, ImageFont  # This import may be needed if you're processing images

from PIL import Image

def process_image(image):
    # Convert PIL image to bytes to send in POST request
    img_bytes = io.BytesIO()
    image.save(img_bytes, format="JPEG")
    img_bytes.seek(0)
    
    
    url = "http://127.0.0.1:9000/deepfake_image"
    files = {'image': img_bytes}
    result = requests.post(url=url, files=files)
    if result.ok:
        json_result = result.json()
        if json_result.get("resultCode") == "Error":
            return {"resultCode": "Error", "result": "Failed to process image"}

        status = json_result.get("result").get("status")
        confidence = json_result.get("result").get("confidence")
        prediction = json_result.get("result").get("prediction")
        similarity = json_result.get("result").get("similarity")
        media_type = json_result.get("result").get("media_type")
        if status == "Not AI Generated":
            status_html = f'<span style="color:green; font-weight:bold;">{status}</span>'
            confidence_html = f'<span style="color:green; font-weight:bold;">{confidence} %</span>'
        else:
            status_html = f'<span style="color:red; font-weight:bold;">{status}</span>'
            confidence_html = f'<span style="color:red; font-weight:bold;">{confidence} %</span>'

        html = ("<table>"
                    "<tr>"
                        "<th>Evaluation Field</th>"
                        "<th>Value</th>"
                    "</tr>"
                    "<tr>"
                        "<td>Result</td>"
                        "<td>{status_html}</td>"
                    "</tr>"
                    "<tr>"
                        "<td>Confidence</td>"
                        "<td>{confidence_html}</td>"
                    "</tr>"
                    "<tr>"
                        "<td>Similarity</td>"
                        "<td>{similarity} % matches to known AI images</td>"
                    "</tr>"
                    "<tr>"
                        "<td>Media Type</td>"
                        "<td>{media_type}</td>"
                    "</tr>"
                    "</table>".format(status_html=status_html, confidence_html=confidence_html, similarity=similarity, media_type=media_type))

        if status == "Ok":
            # Update json_result with the modified process_results
            json_result["result"] = process_results
        return html
    else:
        return {"resultCode": "Error", "result": result.text}
    
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type='pil')
            
            gr.Examples(['examples/1.jpg', 'examples/2.jpg', 'examples/3.jpg'], 
                        inputs=image_input)
            process_button = gr.Button("Process")
        with gr.Column():
            json_output = gr.HTML()
    
    process_button.click(process_image, inputs=[image_input], outputs=[json_output])
    gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fml13571-test1.hf.space"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fml13571-test1.hf.space&label=VISITORS&countColor=%23263759" /></a>')

demo.launch(server_name="0.0.0.0", server_port=7860)