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app.py

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+import os
+import time
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import gradio as gr
+
+# ─── Constants ────────────────────────────────────────────────────────────────
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODELS_DIR = os.path.join(BASE_DIR, "models")
+IMG_SIZE = (224, 224)
+
+MODEL_CONFIGS = [
+    ("MobileNetV3 Small",  "mobilenetv3_small.tflite"),
+    ("MobileNetV3 Large",  "mobilenetv3_large.tflite"),
+    ("EfficientNetV2-B0",  "efficientnetv2b0.tflite"),
+]
+
+MEDAL = ["🥇", "🥈", "🥉"]
+
+# ─── Load labels ──────────────────────────────────────────────────────────────
+with open(os.path.join(MODELS_DIR, "labels.txt")) as f:
+    CLASS_NAMES = [line.strip() for line in f if line.strip()]
+
+# ─── Load TFLite interpreters at startup ──────────────────────────────────────
+INTERPRETERS: list[tuple[str, tf.lite.Interpreter]] = []
+for display_name, filename in MODEL_CONFIGS:
+    path = os.path.join(MODELS_DIR, filename)
+    interp = tf.lite.Interpreter(model_path=path)
+    interp.allocate_tensors()
+    INTERPRETERS.append((display_name, interp))
+
+print(f"Loaded {len(INTERPRETERS)} TFLite models.")
+
+
+# ─── Inference helpers ────────────────────────────────────────────────────────
+def preprocess(pil_image: Image.Image) -> np.ndarray:
+    """Resize, convert to float32 array, add batch dim. Values in [0, 255]."""
+    img = pil_image.convert("RGB").resize(IMG_SIZE, Image.BILINEAR)
+    arr = np.array(img, dtype=np.float32)          # (224, 224, 3)
+    return np.expand_dims(arr, axis=0)              # (1, 224, 224, 3)
+
+
+def run_model(interp: tf.lite.Interpreter, img_array: np.ndarray) -> tuple[np.ndarray, float]:
+    """Run a single TFLite inference. Returns (probs, latency_ms)."""
+    input_idx  = interp.get_input_details()[0]["index"]
+    output_idx = interp.get_output_details()[0]["index"]
+    interp.set_tensor(input_idx, img_array)
+    t0 = time.perf_counter()
+    interp.invoke()
+    latency_ms = (time.perf_counter() - t0) * 1000
+    probs = interp.get_tensor(output_idx)[0]        # shape (num_classes,)
+    return probs, latency_ms
+
+
+def fmt_model_card(display_name: str, probs: np.ndarray, latency_ms: float) -> str:
+    """Build a Markdown string for one model's result card."""
+    top3_idx = np.argsort(probs)[::-1][:3]
+    lines = [f"### {display_name}", f"⏱ `{latency_ms:.1f} ms`", ""]
+    for rank, idx in enumerate(top3_idx):
+        pct = probs[idx] * 100
+        bar = "█" * int(pct / 5)            # simple bar, max 20 chars
+        lines.append(f"{MEDAL[rank]} **{CLASS_NAMES[idx]}** — {pct:.1f}%")
+        lines.append(f"`{bar:<20}` ")
+        lines.append("")
+    return "\n".join(lines)
+
+
+# ─── Main prediction function ─────────────────────────────────────────────────
+def predict(pil_image):
+    if pil_image is None:
+        empty = "_(upload an image and press Run Inference)_"
+        return empty, empty, empty
+
+    img_array = preprocess(pil_image)
+    cards: list[str] = []
+
+    for display_name, interp in INTERPRETERS:
+        probs, latency_ms = run_model(interp, img_array)
+        cards.append(fmt_model_card(display_name, probs, latency_ms))
+
+    return cards[0], cards[1], cards[2]
+
+
+# ─── Gradio UI ────────────────────────────────────────────────────────────────
+CSS = """
+.model-card { background: var(--block-background-fill); border-radius: 12px; padding: 16px; }
+footer { display: none !important; }
+"""
+
+with gr.Blocks(title="Animal Toy Classifier") as demo:
+    gr.Markdown("# 🦁 Animal Toy Classifier\nUpload a photo of an animal toy — three models will predict the class and vote on the best answer.")
+
+    with gr.Row():
+        # ── Left: upload + button ──────────────────────────────────────────────
+        with gr.Column(scale=1, min_width=260):
+            img_in = gr.Image(
+                type="pil",
+                sources=["upload"],
+                label="Input Image",
+                height=280,
+            )
+            with gr.Row():
+                run_btn   = gr.Button("▶  Run Inference", variant="primary", scale=3)
+                clear_btn = gr.ClearButton(
+                    components=[img_in],
+                    value="✕ Clear",
+                    scale=1,
+                )
+
+        # ── Right: per-model result cards ──────────────────────────────────────
+        with gr.Column(scale=2):
+            with gr.Row():
+                with gr.Column(elem_classes="model-card"):
+                    out_small = gr.Markdown("### MobileNetV3 Small\n\n_(waiting)_")
+                with gr.Column(elem_classes="model-card"):
+                    out_large = gr.Markdown("### MobileNetV3 Large\n\n_(waiting)_")
+                with gr.Column(elem_classes="model-card"):
+                    out_eff   = gr.Markdown("### EfficientNetV2-B0\n\n_(waiting)_")
+
+    # ── Wire up ────────────────────────────────────────────────────────────────
+    run_btn.click(
+        fn=predict,
+        inputs=[img_in],
+        outputs=[out_small, out_large, out_eff],
+    )
+
+    # Reset outputs when Clear is pressed
+    clear_btn.click(
+        fn=lambda: (
+            "### MobileNetV3 Small\n\n_(waiting)_",
+            "### MobileNetV3 Large\n\n_(waiting)_",
+            "### EfficientNetV2-B0\n\n_(waiting)_",
+        ),
+        inputs=[],
+        outputs=[out_small, out_large, out_eff],
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Soft(), css=CSS)