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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModel
from PIL import Image
from torchvision import transforms
import json
from torch import nn
from typing import Literal
# Define Multimodal Classifier
class MultimodalClassifier(nn.Module):
def __init__(
self,
text_encoder_id_or_path: str,
image_encoder_id_or_path: str,
projection_dim: int,
fusion_method: Literal["concat", "align", "cosine_similarity"] = "concat",
proj_dropout: float = 0.1,
fusion_dropout: float = 0.1,
num_classes: int = 1,
) -> None:
super().__init__()
self.fusion_method = fusion_method
self.projection_dim = projection_dim
self.num_classes = num_classes
# Text Encoder
self.text_encoder = AutoModel.from_pretrained(text_encoder_id_or_path)
self.text_projection = nn.Sequential(
nn.Linear(self.text_encoder.config.hidden_size, self.projection_dim),
nn.Dropout(proj_dropout),
)
# Image Encoder
self.image_encoder = AutoModel.from_pretrained(image_encoder_id_or_path, trust_remote_code=True)
self.image_encoder.classifier = nn.Identity() # Remove classification head
self.image_projection = nn.Sequential(
nn.Linear(512, self.projection_dim),
nn.Dropout(proj_dropout),
)
# Fusion Layer
fusion_input_dim = self.projection_dim * 2 if fusion_method == "concat" else self.projection_dim
self.fusion_layer = nn.Sequential(
nn.Dropout(fusion_dropout),
nn.Linear(fusion_input_dim, self.projection_dim),
nn.GELU(),
nn.Dropout(fusion_dropout),
)
# Classification Layer
self.classifier = nn.Linear(self.projection_dim, self.num_classes)
def forward(self, pixel_values: torch.Tensor, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
# Text Encoder Projection
full_text_features = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask, return_dict=True).last_hidden_state
full_text_features = full_text_features[:, 0, :] # CLS token
full_text_features = self.text_projection(full_text_features)
# Image Encoder Projection
resnet_image_features = self.image_encoder(pixel_values=pixel_values).last_hidden_state
resnet_image_features = resnet_image_features.mean(dim=[-2, -1]) # Global average pooling
resnet_image_features = self.image_projection(resnet_image_features)
# Fusion
if self.fusion_method == "concat":
fused_features = torch.cat([full_text_features, resnet_image_features], dim=-1)
else:
fused_features = full_text_features * resnet_image_features
# Classification
fused_features = self.fusion_layer(fused_features)
classification_output = self.classifier(fused_features)
return classification_output
# Load the model
def load_model():
with open("config.json", "r") as f:
config = json.load(f)
model = MultimodalClassifier(
text_encoder_id_or_path=config["text_encoder_id_or_path"],
image_encoder_id_or_path="microsoft/resnet-34",
projection_dim=config["projection_dim"],
fusion_method=config["fusion_method"],
proj_dropout=config["proj_dropout"],
fusion_dropout=config["fusion_dropout"],
num_classes=config["num_classes"]
)
checkpoint = torch.load("model_weights.pth", map_location=torch.device('cpu'))
model.load_state_dict(checkpoint, strict=False)
return model
# Load model and tokenizer
model = load_model()
model.eval()
text_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Image transform pipeline
image_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Prediction function
def predict(image: Image.Image, text: str) -> str:
# Process text input
text_inputs = text_tokenizer(
text,
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512
)
# Process image input
image_input = image_transform(image).unsqueeze(0) # Add batch dimension
# Model inference
with torch.no_grad():
classification_output = model(
pixel_values=image_input,
input_ids=text_inputs["input_ids"],
attention_mask=text_inputs["attention_mask"]
)
predicted_class = torch.sigmoid(classification_output).round().item()
return "Fake News" if predicted_class == 1 else "Real News"
# Gradio Interface
interface = gr.Interface(
fn=predict,
inputs=[
gr.Image(type="pil", label="Upload Related Image"),
gr.Textbox(lines=2, placeholder="Enter news text for classification...", label="Input Text")
],
outputs=gr.Label(label="Prediction"),
title="Fake News Detector",
description="Upload an image and provide text to classify the news as 'Fake' or 'Real'."
)
interface.launch()
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