Delete alphabet_sign_language_detection.py

alphabet_sign_language_detection.py

@@ -1,45 +0,0 @@
-import gradio as gr
-import spaces
-from transformers import AutoImageProcessor
-from transformers import SiglipForImageClassification
-from transformers.image_utils import load_image
-from PIL import Image
-import torch
-
-# Load model and processor
-model_name = "prithivMLmods/Alphabet-Sign-Language-Detection"
-model = SiglipForImageClassification.from_pretrained(model_name)
-processor = AutoImageProcessor.from_pretrained(model_name)
-
-@spaces.GPU
-def sign_language_classification(image):
-    """Predicts sign language alphabet category for an image."""
-    image = Image.fromarray(image).convert("RGB")
-    inputs = processor(images=image, return_tensors="pt")
-    
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
-    
-    labels = {
-        "0": "A", "1": "B", "2": "C", "3": "D", "4": "E", "5": "F", "6": "G", "7": "H", "8": "I", "9": "J",
-        "10": "K", "11": "L", "12": "M", "13": "N", "14": "O", "15": "P", "16": "Q", "17": "R", "18": "S", "19": "T",
-        "20": "U", "21": "V", "22": "W", "23": "X", "24": "Y", "25": "Z"
-    }
-    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
-    
-    return predictions
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=sign_language_classification,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Label(label="Prediction Scores"),
-    title="Alphabet Sign Language Detection",
-    description="Upload an image to classify it into one of the 26 sign language alphabet categories."
-)
-
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()