upload 1683864255 model

README.md

@@ -1,12 +1,11 @@
 ---
-title: Tfx Vit Pipeline
-emoji: 🐨
-colorFrom: gray
-colorTo: pink
+title: Leaf Classification
+emoji: 🍂
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 3.29.0
 app_file: app.py
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+license: apache-2.0
+---

app.py

@@ -0,0 +1,83 @@
+import tarfile
+import wandb
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from transformers import ViTFeatureExtractor
+
+PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
+feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
+
+MODEL = None 
+
+RESOLTUION = 224
+
+labels = []
+
+with open(r"labels.txt", "r") as fp:
+    for line in fp:
+        labels.append(line[:-1])
+
+def normalize_img(
+    img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
+):
+    img = img / 255
+    mean = tf.constant(mean)
+    std = tf.constant(std)
+    return (img - mean) / std
+
+def preprocess_input(image):
+    image = np.array(image)
+    image = tf.convert_to_tensor(image)
+
+    image = tf.image.resize(image, (RESOLTUION, RESOLTUION))
+    image = normalize_img(image)
+
+    image = tf.transpose(
+        image, (2, 0, 1)
+    )  # Since HF models are channel-first.
+
+    return {
+        "pixel_values": tf.expand_dims(image, 0)
+    }
+
+def get_predictions(wb_token, image):
+    global MODEL
+    
+    if MODEL is None:
+        wandb.login(key=wb_token)
+        wandb.init(project="tfx-vit-pipeline")
+        path = wandb.use_artifact('tfx-vit-pipeline/final_model:1683864255', type='model').download()
+
+        tar = tarfile.open(f"{path}/model.tar.gz")
+        tar.extractall(path=".")
+
+        MODEL = tf.keras.models.load_model("./model")
+    
+    preprocessed_image = preprocess_input(image)
+    prediction = MODEL.predict(preprocessed_image)
+    probs = tf.nn.softmax(prediction['logits'], axis=1)
+
+    confidences = {labels[i]: float(probs[0][i]) for i in range(3)}
+    return confidences
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Simple demo for a Image Classification of the Beans Dataset with HF ViT model")
+
+    wb_token_if = gr.Textbox(interactive=True, label="Your Weight & Biases API Key")
+
+    with gr.Row():
+        image_if = gr.Image()
+        label_if = gr.Label(num_top_classes=3)
+
+    classify_if = gr.Button()
+
+    classify_if.click(
+        get_predictions,
+        [wb_token_if, image_if],
+        label_if
+    )
+
+demo.launch(debug=True)

labels.txt

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+angular_leaf_spot
+bean_rust
+healthy

requirements.txt

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+tensorflow
+transformers
+wandb