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README.md

@@ -1,12 +1 @@
----
-title: Microbeguru
-emoji: 💻
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 4.28.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MicroGuru

microguru.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load the pre-trained model\n",
+    "model = tf.keras.models.load_model(\"\")\n",
+    "\n",
+    "# Define the prediction function\n",
+    "def classify_image(image):\n",
+    "    # Preprocess the image\n",
+    "    image = tf.image.resize(image, (224, 224))\n",
+    "    image = tf.keras.applications.mobilenet_v2.preprocess_input(image)\n",
+    "    image = np.expand_dims(image, axis=0)\n",
+    "\n",
+    "    # Make predictions\n",
+    "    predictions = model.predict(image)\n",
+    "    label = np.argmax(predictions[0])\n",
+    "    confidence = predictions[0][label]\n",
+    "\n",
+    "    return label, confidence\n",
+    "\n",
+    "# Create the Gradio interface\n",
+    "inputs = gr.inputs.Image()\n",
+    "outputs = gr.outputs.Label(num_top_classes=3)\n",
+    "\n",
+    "# Launch the interface with a public link\n",
+    "gr.Interface(\n",
+    "    fn=classify_image, \n",
+    "    inputs=inputs, \n",
+    "    outputs=outputs, \n",
+    "    title=\"Micro-Guru\", \n",
+    "    description=\"Upload an image and the model will predict its class along with the confidence level.\"\n",
+    ").launch(share=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

microgurutestmodel.h5

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