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app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+import time
+import os
+
+os.environ["STREAMLIT_CACHE_DIR"] = "/tmp/streamlit-cache"
+
+st.title("🌿 MobileNet TFLite Image Classifier")
+st.write("Upload an image to test your quantized MobileNet model.")
+
+
+def load_labels(path):
+    try:
+        with open(path, "r") as f:
+            return [line.strip() for line in f.readlines()]
+    except FileNotFoundError:
+        return None
+
+labels = load_labels("class_names.txt")
+
+def preprocess(image_array: np.ndarray) -> np.ndarray:
+    """Replicate keras.applications.mobilenet_v3.preprocess_input"""
+    image_array = image_array.astype(np.float32)
+    image_array = image_array / 127.5 - 1.0  # scale to [-1, 1]
+    return image_array
+
+@st.cache_resource
+def load_tflite_model():
+    interpreter = Interpreter(model_path="models/mobilenet_int8.tflite")
+    interpreter.allocate_tensors()
+    return interpreter
+interpreter = load_tflite_model()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+
+uploaded_file = st.file_uploader("📸 Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", width="stretch")
+
+  
+
+    if st.button("🔍 Predict"):
+        with st.spinner("Analyzing image..."):
+            img = image.resize((224, 224))
+            img = np.array(img)
+            input_data = preprocess(img)
+            input_data = np.expand_dims(img, axis=0).astype(np.float32)
+
+            start = time.time()
+            interpreter.set_tensor(input_details[0]['index'], input_data)
+            interpreter.invoke()
+            preds = interpreter.get_tensor(output_details[0]['index'])[0]
+            inference_time = (time.time() - start) * 1000
+
+            top_k = preds.argsort()[-3:][::-1]
+            st.markdown("### 🌱 Predictions:")
+            for i in top_k:
+                label = labels[i] if labels else f"Class {i}"
+                st.write(f"**{label}** — {preds[i] * 100:.2f}%")
+                if preds[i]==1:
+                    break
+            st.info(f"⚡ Inference Time: {inference_time:.2f} ms")