Spaces:
Running
Running
File size: 3,453 Bytes
2e8ccd8 18855ba 23d88ae 18855ba 23d88ae 2e8ccd8 23d88ae 2e8ccd8 18855ba 2e8ccd8 23d88ae 2e8ccd8 23d88ae 2e8ccd8 23d88ae 2e8ccd8 23d88ae 2e8ccd8 23d88ae 2e8ccd8 23d88ae 2e8ccd8 18855ba 23d88ae 18855ba 23d88ae 18855ba 23d88ae 2e8ccd8 23d88ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
import os
import gradio as gr
import json
from gradio_client import Client, handle_file
# Validate environment variables and initialize backend client
BACKEND_URL = os.getenv("BACKEND")
HF_TOKEN = os.getenv("TOKEN")
if not BACKEND_URL:
raise ValueError(
"BACKEND environment variable is not set. "
"Please set it to the backend URL (e.g., 'https://your-backend-url')"
)
try:
backend = Client(BACKEND_URL, hf_token=HF_TOKEN)
except Exception as e:
raise Exception(f"Failed to initialize backend client: {str(e)}")
def detect(image):
"""Detect deepfake content in an image with comprehensive error handling"""
if image is None:
raise gr.Error("Please upload an image to analyze")
try:
result_text = backend.predict(
image=handle_file(image),
api_name="/detect"
)
result = json.loads(result_text)
if not result or result.get("status") != "ok":
raise gr.Error("Analysis failed: Invalid response from backend")
overall = f"{result['overall']}% Confidence"
aigen = f"{result['aigen']}% (AI-Generated Content Likelihood)"
deepfake = f"{result['deepfake']}% (Face Manipulation Likelihood)"
return overall, aigen, deepfake
except json.JSONDecodeError:
raise gr.Error("Error processing analysis results")
except Exception as e:
raise gr.Error(f"Analysis error: {str(e)}")
# [Rest of your CSS and UI code remains the same...]
# I'll include just the essential setup part here for brevity
custom_css = """
.container {
max-width: 1200px;
margin: 0 auto;
padding: 20px;
font-family: 'Arial', sans-serif;
}
.header {
color: #2c3e50;
border-bottom: 2px solid #3498db;
padding-bottom: 10px;
}
.button-gradient {
background: linear-gradient(45deg, #3498db, #2ecc71, #9b59b6);
background-size: 400% 400%;
border: none;
padding: 12px 24px;
font-size: 16px;
font-weight: 600;
color: white;
border-radius: 8px;
cursor: pointer;
transition: all 0.3s ease;
animation: gradientAnimation 3s ease infinite;
box-shadow: 0 2px 8px rgba(52, 152, 219, 0.3);
}
.button-gradient:hover {
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(52, 152, 219, 0.5);
}
@keyframes gradientAnimation {
0% { background-position: 0% 50%; }
50% { background-position: 100% 50%; }
100% { background-position: 0% 50%; }
}
"""
MARKDOWN0 = """
<div class="header">
<h1>DeepFake Detection System</h1>
<p>Advanced AI-powered analysis for identifying manipulated media</p>
</div>
"""
with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
gr.Markdown(MARKDOWN0)
with gr.Row(elem_classes="container"):
with gr.Column(scale=1):
image = gr.Image(type='filepath', height=400, label="Upload Image")
detect_button = gr.Button("Analyze Image", elem_classes="button-gradient")
with gr.Column(scale=2):
overall = gr.Label(label="Confidence Score")
aigen = gr.Label(label="AI-Generated Content")
deepfake = gr.Label(label="Face Manipulation")
detect_button.click(
fn=detect,
inputs=[image],
outputs=[overall, aigen, deepfake]
)
demo.queue(api_open=False, concurrency_count=8).launch(
server_name="0.0.0.0",
show_api=False,
debug=True
) |