Upload 3 files

uploads gradio app

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example_input.png filter=lfs diff=lfs merge=lfs -text

gradio_app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+from transformers import pipeline
+
+# Lade DEIN Modell
+vit_classifier = pipeline("image-classification", model="LindiSimon/vit-beans-model")
+clip_detector = pipeline(model="openai/clip-vit-large-patch14", task="zero-shot-image-classification")
+
+labels_beans = ["angular_leaf_spot", "bean_rust", "healthy"]
+
+def classify_bean(image):
+    vit_results = vit_classifier(image)
+    vit_output = {result['label']: result['score'] for result in vit_results}
+
+    clip_results = clip_detector(image, candidate_labels=labels_beans)
+    clip_output = {result['label']: result['score'] for result in clip_results}
+
+    return {"ViT Classification": vit_output, "CLIP Zero-Shot Classification": clip_output}
+
+examples = [["example_input.png"]]
+
+iface = gr.Interface(
+    fn=classify_bean,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.JSON(),
+    title="Bean Disease Classification",
+    description="Vergleich eines trainierten ViT-Modells mit CLIP für Bean-Disease-Klassifikation.",
+    examples=examples
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+transformers
+torch