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0205739 | 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 | import gradio as gr
import torch
from transformers import AutoTokenizer
# Import your modularized code
from model import MultiModalDetector
from utils import collate_twibot_batch
# 1. Setup Device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Initializing Application on {device}...")
# 2. Build Model & Load Weights
model = MultiModalDetector(text_dim=768, vision_dim=512, graph_dim=128)
model_path = "phase2_production_model.pth"
try:
model.load_state_dict(torch.load(model_path, map_location=device))
print(f"✅ Success: Weights loaded from {model_path}")
except Exception as e:
print(f"❌ Error: Could not load weights. Ensure '{model_path}' is in the same directory.")
print(e)
model = model.to(device)
model.eval()
# 3. Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
# 4. Define the Inference Function
def gradio_predict(profile_id, bio, tweets, following_count, follower_count, image_url):
following_list = [f"f_{i}" for i in range(int(following_count))]
follower_list = [f"fl_{i}" for i in range(int(follower_count))]
custom_data = {
"ID": profile_id,
"profile": {
"description": bio,
"profile_image_url": image_url
},
"tweet": [t.strip() for t in tweets.split("|") if t.strip()],
"neighbor": {
"following": following_list,
"follower": follower_list
},
"label": 0
}
try:
text_inputs, images, graphs, _ = collate_twibot_batch([custom_data], tokenizer)
text_inputs = {k: v.to(device) for k, v in text_inputs.items()}
images = images.to(device)
graphs = graphs.to(device)
with torch.no_grad():
logits = model(text_inputs, images, graphs)
logits = torch.clamp(logits, min=-10.0, max=10.0)
probability = torch.sigmoid(logits).item()
is_bot = probability >= 0.5
prediction = "🤖 BOT (Fake Profile)" if is_bot else "👤 HUMAN (Real Profile)"
confidence = (probability if is_bot else (1.0 - probability)) * 100
return f"{prediction}\nConfidence Score: {confidence:.2f}%"
except Exception as e:
return f"Error analyzing profile: {e}"
# 5. Build and Launch the Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🕵️ Multi-Modal Fake Profile Detector")
gr.Markdown("Enter the details of a social media account below to analyze if it is a Bot or a Human.")
with gr.Row():
with gr.Column():
prof_id = gr.Textbox(label="Username", placeholder="@username")
bio = gr.Textbox(label="Bio / Description", lines=2, placeholder="e.g., Crypto enthusiast. DM for 100x returns!")
tweets = gr.Textbox(label="Recent Tweets (separate with | )", lines=3, placeholder="Tweet 1 | Tweet 2 | Tweet 3")
img_url = gr.Textbox(label="Profile Image URL", placeholder="https://...")
with gr.Row():
following = gr.Number(label="Following Count", value=100, precision=0)
followers = gr.Number(label="Follower Count", value=10, precision=0)
submit_btn = gr.Button("🔍 Analyze Profile", variant="primary")
with gr.Column():
output_res = gr.Textbox(label="Detection Result", lines=3)
submit_btn.click(gradio_predict,
inputs=[prof_id, bio, tweets, following, followers, img_url],
outputs=output_res)
# Standard Python execution block
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
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