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README.md

@@ -1,12 +1,20 @@
 ---
-title: ASL
-emoji: 🐢
-colorFrom: purple
-colorTo: red
+title: ASL Letter Classifier
+emoji: 🖐️
+colorFrom: green
+colorTo: green
 sdk: gradio
 sdk_version: 5.49.1
 app_file: app.py
-pinned: false
+pinned: true
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🖐️ ASL Letter Classifier
+
+This app uses:
+
+- **YOLOv11 Classification** (fine-tuned on ASL alphabet images)
+- **MediaPipe Hand Landmarks** for annotation
+- **Gradio Web UI** for uploading and testing images
+
+Upload a photo of a hand sign to see the detected letter and confidence.

app.py

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+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)

best (1).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:607c9e1545dba54efee9c9985310c30c94b74f33173b32367689cf5a43d768f2
+size 3246722

gitattributes

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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+ultralytics
+torch
+gradio
+mediapipe
+requests

yolov11n_finetuned_ASL.pt

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+oid sha256:4f5f1e19e26f3024d8b7ba2df598bf6981728cdc6ebd133975a85af08e5682c2
+size 3246722