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README.md

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----
-title: Irisbuddy
-emoji: 🐢
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 5.45.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# app.py
+import gradio as gr
+import onnxruntime as ort
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import cv2
+import io
+
+# Load labels
+with open("labels.txt", "r") as f:
+    LABELS = [x.strip() for x in f.readlines()]
+
+# Load ONNX model
+sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
+
+# Helper: simple preprocess - adapt to your model's needs
+def preprocess(pil_img, input_size=(640,640)):
+    img = pil_img.convert("RGB")
+    img = img.resize(input_size)
+    np_img = np.array(img).astype(np.float32) / 255.0   # normalize if model expected 0-1
+    # Change to channels-first if model expects (1,C,H,W)
+    np_img = np.transpose(np_img, (2,0,1))[np.newaxis, ...]
+    return np_img
+
+# Helper: very basic NMS and postprocess - adapt as necessary
+def postprocess(outputs, orig_w, orig_h, conf_threshold=0.3, iou_threshold=0.45):
+    # This section depends on your model outputs.
+    # Example: suppose outputs[0] -> [N, 6] with (x1,y1,x2,y2,score,class)
+    preds = outputs[0]
+    boxes = []
+    for row in preds:
+        x1,y1,x2,y2,score,cls = row
+        if score < conf_threshold:
+            continue
+        # scale coords back to original image size if your model used 640x640
+        boxes.append({
+            "box": [x1*orig_w, y1*orig_h, x2*orig_w, y2*orig_h],
+            "score": float(score),
+            "class": int(cls)
+        })
+    # (Optional) Apply NMS here if model doesn't already do it
+    return boxes
+
+def draw_boxes(pil_img, boxes):
+    img = pil_img.convert("RGB")
+    draw = ImageDraw.Draw(img)
+    for b in boxes:
+        x1,y1,x2,y2 = b["box"]
+        label = LABELS[b["class"]] if 0 <= b["class"] < len(LABELS) else str(b["class"])
+        draw.rectangle([x1,y1,x2,y2], outline="red", width=3)
+        draw.text((x1, y1-10), f"{label} {b['score']:.2f}", fill="red")
+    return img
+
+def predict(image):
+    if image is None:
+        return None, "No image"
+    pil = Image.fromarray(image.astype('uint8')) if isinstance(image, np.ndarray) else Image.open(io.BytesIO(image.read()))
+    orig_w, orig_h = pil.size
+    input_tensor = preprocess(pil)  # adapt input_size if needed
+
+    # Run ONNX
+    input_name = sess.get_inputs()[0].name
+    outputs = sess.run(None, {input_name: input_tensor})
+    # Postprocess according to your model's output structure
+    boxes = postprocess(outputs, orig_w, orig_h)
+    out_img = draw_boxes(pil, boxes)
+    txt = "\n".join([f"{LABELS[b['class']]}: {b['score']:.2f}" for b in boxes]) if boxes else "No detections"
+    return out_img, txt
+
+# Gradio UI
+title = "ONNX Demo"
+desc = "Upload an image or use webcam. Adapt preprocessing/postprocessing per your model."
+
+iface = gr.Interface(fn=predict,
+                     inputs=gr.Image(source="upload", tool="editor", type="numpy"),
+                     outputs=[gr.Image(type="pil"), gr.Textbox()],
+                     title=title,
+                     description=desc,
+                     examples=None)
+
+if __name__ == "__main__":
+    iface.launch()

labels.txt

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+car
+curb
+motorcycle
+pothole
+pedestrian
+step
+post
+obstacle

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4daa0df1cbd53d9addf692c553ddb885ffe1e4b68d78de58c7eb423439c8e5e2
+size 12938955

requirements.txt

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+gradio>=3.0
+onnxruntime
+numpy
+pillow
+opencv-python-headless