Upload 4 files

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load model
+interpreter = tf.lite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Load labels
+with open("labels.txt", "r") as f:
+    labels = [line.strip() for line in f.readlines()]
+
+def predict(image):
+    # Preprocess image
+    image = image.resize((224, 224))  # adjust size if needed
+    image_array = np.array(image, dtype=np.float32) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+
+    # Run inference
+    interpreter.set_tensor(input_details[0]['index'], image_array)
+    interpreter.invoke()
+    predictions = interpreter.get_tensor(output_details[0]['index'])[0]
+
+    # Map predictions to labels
+    results = {labels[i]: float(predictions[i]) for i in range(len(labels))}
+    return results
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Moodmate",
+    description="Upload an image to detect the mood."
+)
+
+if __name__ == "__main__":
+    demo.launch()

labels.txt

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+0 Class 1
+1 Class 8
+2 Class 9
+3 Class 10
+4 Class 11
+5 Class 12
+6 Class 13
+7 Class 14
+8 Class 15
+9 Class 16
+10 Class 17
+11 Class 18

model_unquant.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:309d07c5283dd85717f3a1c12fac3695b7b8ba8833a3b5b96b06cfa386058775
+size 2094332

requirements.txt

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+tensorflow
+numpy
+pillow
+gradio