Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""sign_identifier_gradio_final.ipynb
+
+Gradio interface for image classification using a classmate’s model.
+Model: cassieli226/sign-identification-automl
+"""
+
+# !pip install autogluon.multimodal gradio huggingface_hub pillow pandas --quiet
+
+import os, pathlib, shutil, zipfile, tempfile, io
+import pandas as pd
+from PIL import Image
+import gradio as gr
+import huggingface_hub
+from autogluon.multimodal import MultiModalPredictor
+
+# -----------------------------
+# Config
+# -----------------------------
+MODEL_REPO_ID = "cassieli226/sign-identification-automl"
+ZIP_FILENAME = "autogluon_predictor_dir.zip"
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+MAX_SIZE_MB = 5
+
+# -----------------------------
+# Model loading
+# -----------------------------
+def prepare_predictor_dir() -> str:
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    local_zip = huggingface_hub.hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=ZIP_FILENAME,
+        repo_type="model",
+        local_dir=str(CACHE_DIR),
+        local_dir_use_symlinks=False,
+    )
+    if EXTRACT_DIR.exists():
+        shutil.rmtree(EXTRACT_DIR)
+    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
+    with zipfile.ZipFile(local_zip, "r") as zf:
+        zf.extractall(str(EXTRACT_DIR))
+    contents = list(EXTRACT_DIR.iterdir())
+    return str(contents[0]) if (len(contents) == 1 and contents[0].is_dir()) else str(EXTRACT_DIR)
+
+print("Loading predictor...")
+PREDICTOR_DIR = prepare_predictor_dir()
+PREDICTOR = MultiModalPredictor.load(PREDICTOR_DIR)
+print("✅ Model loaded!")
+
+# Try to extract readable class names
+try:
+    if hasattr(PREDICTOR, "label_generator") and hasattr(PREDICTOR.label_generator, "category_map"):
+        CLASS_MAP = {str(k): str(v) for k, v in PREDICTOR.label_generator.category_map.items()}
+    else:
+        CLASS_MAP = {str(i): str(lbl) for i, lbl in enumerate(PREDICTOR.class_labels)}
+except Exception:
+    CLASS_MAP = {}
+print("Class map:", CLASS_MAP)
+
+# -----------------------------
+# Helpers
+# -----------------------------
+def _pil_to_tmp(img: Image.Image, resize_size=224) -> str:
+    img = img.convert("RGB").resize((resize_size, resize_size))
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    img.save(tmp.name, format="PNG")
+    return tmp.name
+
+def _size_mb_of_png(img: Image.Image) -> float:
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    return buf.tell() / (1024 * 1024)
+
+# -----------------------------
+# Inference
+# -----------------------------
+def predict_image(img, resize_size=224, top_k=3, prob_threshold=0.05):
+    if img is None:
+        return None, "<div style='color:#b91c1c'>⚠️ Please upload an image.</div>"
+
+    # Validate size
+    size_mb = _size_mb_of_png(img)
+    if size_mb > MAX_SIZE_MB:
+        return None, f"<div style='color:#b91c1c'>⚠️ File too large: {size_mb:.2f} MB (limit {MAX_SIZE_MB} MB).</div>"
+
+    # Preprocess
+    img_path = _pil_to_tmp(img, resize_size)
+    df = pd.DataFrame({"image": [img_path]})
+
+    # Predict probabilities
+    proba_df = PREDICTOR.predict_proba(df)
+    probs = proba_df.iloc[0].sort_values(ascending=False)
+
+    # Map numeric indices to actual category names
+    probs.index = [CLASS_MAP.get(str(i), str(i)) for i in probs.index]
+
+    # Apply threshold + top-k
+    filtered = probs[probs > prob_threshold]
+    top = filtered.head(top_k) if not filtered.empty else probs.head(top_k)
+
+    # Top-1
+    top_label = top.index[0]
+    top_conf = float(top.iloc[0]) * 100
+
+    # HTML result
+    html = f"""
+    <div style="padding:20px;background:#f0f9ff;border-radius:12px;border-left:5px solid #3b82f6;">
+        <h2 style="color:#1e40af;margin:0 0 12px;">🔎 Prediction Results</h2>
+        <div style="background:#3b82f6;color:white;padding:15px;border-radius:10px;margin-bottom:15px;text-align:center;">
+            <div style="font-size:18px;">Predicted Sign</div>
+            <div style="font-size:36px;font-weight:800;letter-spacing:.3px;">{top_label}</div>
+            <div style="font-size:16px;opacity:.95;">Confidence: {top_conf:.1f}%</div>
+        </div>
+        <h4 style="color:#1e40af;margin:10px 0;">Top {len(top)} Predictions</h4>
+        <ul style="margin:0 0 10px 18px;color:#111827;">
+    """
+    for cls, prob in top.items():
+        html += f"<li><b>{cls}</b>: {prob*100:.1f}%</li>"
+    html += "</ul></div>"
+
+    return img, html
+
+# -----------------------------
+# Gradio UI
+# -----------------------------
+with gr.Blocks(css="""
+    .gradio-container { font-family: 'Segoe UI', system-ui, -apple-system, Arial, sans-serif; }
+""") as demo:
+    gr.HTML(
+        "<h1 style='text-align:center;color:#1e40af;'>🚦 Traffic Sign Identifier</h1>"
+        "<p style='text-align:center;color:#334155;'>Upload a traffic sign image to see predictions.</p>"
+    )
+
+    with gr.Row():
+        with gr.Column():
+            img_in = gr.Image(type="pil", image_mode="RGB", label="Upload Image", sources=["upload","webcam"])
+            resize_size = gr.Slider(64, 512, value=224, step=32, label="Resize Size (px)")
+            top_k = gr.Slider(1, 10, value=3, step=1, label="Top-k Predictions")
+            prob_threshold = gr.Slider(0.0, 0.9, value=0.05, step=0.01, label="Probability Threshold")
+            btn = gr.Button("🔍 Predict", variant="primary")
+        with gr.Column():
+            orig_out = gr.Image(label="Original Image", image_mode="RGB")
+            res_out = gr.HTML(label="Results")
+
+    btn.click(
+        fn=predict_image,
+        inputs=[img_in, resize_size, top_k, prob_threshold],
+        outputs=[orig_out, res_out],
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)