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b0ee9c9 | 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 | import os
import cv2
import numpy as np
import torch
from ultralytics import YOLO
import gradio as gr
import traceback
# -----------------------------
# 1. YOLO model path
# -----------------------------
YOLO_MODEL_PATH = "best.pt" # Push this small model to HF repo
# -----------------------------
# 2. Load YOLO model
# -----------------------------
yolo_model = YOLO(YOLO_MODEL_PATH)
yolo_model.eval()
# -----------------------------
# 3. Inference function
# -----------------------------
def predict_asl(image):
try:
if image is None:
raise ValueError("No image uploaded")
img = image.copy()
h, w, _ = img.shape
print(f"🔹 Uploaded image shape: {img.shape}, dtype: {img.dtype}")
# --- YOLO prediction directly on NumPy array ---
results = yolo_model.predict(img, imgsz=300, verbose=False)[0]
pred_idx = results.probs.top1
pred_label = results.names[pred_idx]
confidence = results.probs.data[pred_idx].item()
# Overlay prediction text on original image
cv2.putText(
img,
f"{pred_label} ({confidence:.2f})",
(10, 30),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 0, 255),
2,
cv2.LINE_AA
)
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), pred_label, round(confidence, 2)
except Exception as e:
print("❌ Error in predict_asl:", e)
traceback.print_exc()
return image, "Error", 0.0
# -----------------------------
# 4. Gradio Interface
# -----------------------------
title = "🖐️ ASL Letter Classifier"
description = "Upload a hand sign image and see the predicted letter and confidence."
iface = gr.Interface(
fn=predict_asl,
inputs=gr.Image(type="numpy"),
outputs=[
gr.Image(type="numpy", label="Original Image with Prediction"),
gr.Textbox(label="Predicted Letter"),
gr.Textbox(label="Confidence")
],
title=title,
description=description,
allow_flagging="never"
)
if __name__ == "__main__":
iface.launch(share=True)
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