Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer
+from PIL import Image
+from torchvision import transforms
+
+# 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 "Biased" if predicted_class == 1 else "Unbiased"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(lines=2, placeholder="Enter text for classification...", label="Input Text")
+    ],
+    outputs=gr.Label(label="Prediction"),
+    title="Multimodal Bias Classifier",
+    description="Upload an image and provide a text to classify it as 'Biased' or 'Unbiased'."
+)
+
+interface.launch()