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import gradio as gr
from transformers import CLIPModel, CLIPProcessor
from PIL import Image
# Step 1: Load Fine-Tuned Model from Hugging Face Model Hub
model_name = "quadranttechnologies/retail-content-safety-clip-finetuned"
print("Initializing the application...")
try:
print("Loading the model from Hugging Face Model Hub...")
model = CLIPModel.from_pretrained(model_name, trust_remote_code=True)
processor = CLIPProcessor.from_pretrained(model_name)
print("Model and processor loaded successfully.")
except Exception as e:
print(f"Error loading the model or processor: {e}")
raise RuntimeError(f"Failed to load model: {e}")
# Step 2: Define the Inference Function
def classify_image(image):
"""
Classify an image as 'safe' or 'unsafe' and return probabilities.
"""
try:
if image is None:
raise ValueError("No image provided. Please upload a valid image.")
# Define categories
categories = ["safe", "unsafe"]
# Process the image
inputs = processor(text=categories, images=image, return_tensors="pt", padding=True)
# Run inference
outputs = model(**inputs)
# Extract logits
logits_per_image = outputs.logits_per_image # Shape: [1, 2]
print(f"Logits: {logits_per_image}")
# Apply softmax to logits to get probabilities
probs = logits_per_image.softmax(dim=1) # Shape: [1, 2]
print(f"Softmax probabilities: {probs}")
# Extract probabilities for each category
safe_prob = probs[0][0].item() # Extract 'safe' probability
unsafe_prob = probs[0][1].item() # Extract 'unsafe' probability
print(f"Safe probability: {safe_prob}, Unsafe probability: {unsafe_prob}")
# Normalize probabilities to ensure they sum to 100%
total_prob = safe_prob + unsafe_prob
print(f"Total probability before normalization: {total_prob}")
safe_percentage = (safe_prob / total_prob) * 100
unsafe_percentage = (unsafe_prob / total_prob) * 100
# Ensure the sum is exactly 100%
print(f"Normalized percentages: Safe={safe_percentage}%, Unsafe={unsafe_percentage}%")
return {
"safe": round(safe_percentage, 2), # Rounded to 2 decimal places
"unsafe": round(unsafe_percentage, 2)
}
except Exception as e:
return {"Error": str(e)}
# Step 3: Set Up Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=2), # Use gr.Label to display probabilities with a bar-style visualization
title="Content Safety Classification",
description="Upload an image to classify it as 'safe' or 'unsafe' with corresponding probabilities.",
)
# Step 4: Launch Gradio Interface
if __name__ == "__main__":
print("Launching the Gradio interface...")
iface.launch()
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