Upload 3 files

.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
+bob_spaces.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load your model
+model = tf.keras.models.load_model('bob_spaces.keras')
+
+
+# Define preprocessing and prediction
+def predict(image):
+    # Preprocess image to match your model's expected input
+    image = image.resize((224, 224))  # Example size
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+
+    # Make prediction
+    prediction = model.predict(image)
+    class_idx = np.argmax(prediction[0])
+
+    # Return as dictionary of class probabilities
+    return {str(i): float(prediction[0][i]) for i in range(len(prediction[0]))}
+
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Image Classifier",
+    description="Upload an image for classification"
+)
+
+iface.launch()

bob_spaces.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc589cb5d7dd5c5197000a5b46c6d2ac96a50c2d55f494f0eeb5cb90c3237d27
+size 29614459

requirements.txt

@@ -0,0 +1,15 @@
+{\rtf1\ansi\ansicpg1252\cocoartf2822
+\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica;}
+{\colortbl;\red255\green255\blue255;}
+{\*\expandedcolortbl;;}
+\margl1440\margr1440\vieww11520\viewh8400\viewkind0
+\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
+
+\f0\fs24 \cf0 tensorflow\
+gradio\
+pillow\
+numpy\
+\
+\
+\
+}