App ready to test on production

app.py

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+from turtle import title
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Initialize the pipeline with your model
+pipe = pipeline("image-classification", model="adonaivera/ofwat_material_classification")
+
+def classify_image(image):
+    # Convert the input image to PIL format
+    PIL_image = Image.fromarray(image).convert('RGB')
+    
+    # Classify the image using the pipeline
+    res = pipe(PIL_image)
+    
+    # Extract labels and scores
+    return {dic["label"]: dic["score"] for dic in res}
+
+# Create the Gradio interface
+iface = gr.Interface(
+    classify_image, 
+    "image", 
+    "label", 
+    examples=[
+        ["examples/CS.jpg"], 
+        ["examples/GI.jpg"],
+        ["examples/PP.jpg"],
+        ["examples/RC.jpg"]
+    ],
+    description="Upload an image to classify its material.",
+    title="Material Classification with AI by Subterra"
+)
+
+# Launch the interface
+iface.launch()

flagged/log.csv

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+image,output 0,output 1,flag,username,timestamp
+"{""path"":""flagged/image/d97b9ae054eab1bd10dc/PP.jpg"",""url"":""http://127.0.0.1:7860/file=/private/var/folders/5q/yl8pmxm116g6r3k8fd9gk74m0000gn/T/gradio/12885a897d79141c54ccb542582449159242eaa0/PP.jpg"",""size"":null,""orig_name"":""PP.jpg"",""mime_type"":null}","{""label"":""VC"",""confidences"":[{""label"":""VC"",""confidence"":0.7706952691078186},{""label"":""PVC"",""confidence"":0.20648105442523956},{""label"":""PE"",""confidence"":0.005161861423403025},{""label"":""PF"",""confidence"":0.004196972120553255},{""label"":""CO"",""confidence"":0.0034184197429567575}]}","| Prefix | Full Name |
+| ------ | --------- |
+| CS | Carbon Steel |
+| GI | Galvanized Iron |
+| PP | Polypropylene |
+| RC | Reinforced Concrete |",,,2023-11-27 15:26:38.548865