app.py

# gradio is the library used to build the web interface
import gradio as gr

# numpy is used for numerical operations
import numpy as np

# ai_edge_litert is Google's official TFLite runtime
from ai_edge_litert.interpreter import Interpreter

# PIL is used for image loading and conversion
from PIL import Image


# ------------------------------------
# LOAD THE MODEL
# ------------------------------------

interpreter = Interpreter(model_path="resnet50_float32.tflite")
interpreter.allocate_tensors()

input_details  = interpreter.get_input_details()
output_details = interpreter.get_output_details()

INPUT_SIZE = (224, 224)

print("Gatekeeper model loaded successfully")


# ------------------------------------
# THRESHOLD
# ------------------------------------

# cervix must score at least 0.55 to be accepted as a positive detection
CERVIX_THRESHOLD = 0.55


# ------------------------------------
# IMAGE PREPROCESSING FUNCTION
# ------------------------------------

def preprocess_image(image):
    img = Image.fromarray(image).convert("RGB").resize(INPUT_SIZE)
    img = np.array(img, dtype=np.float32) / 255.0
    img = np.expand_dims(img, axis=0)
    return img


# ------------------------------------
# CLASSIFICATION FUNCTION
# ------------------------------------

def classify_image(image):
    if image is None:
        return None, "Please upload an image first"

    # preprocess and run inference
    processed = preprocess_image(image)
    interpreter.set_tensor(input_details[0]['index'], processed)
    interpreter.invoke()
    output = interpreter.get_tensor(output_details[0]['index'])

    print(f"Raw model output: {output}")

    prob_non_cervix = float(output[0][0])
    prob_cervix     = float(output[0][1])

    print(f"Non-Cervix: {prob_non_cervix:.4f} | Cervix: {prob_cervix:.4f}")

    # simple threshold check
    if prob_cervix >= CERVIX_THRESHOLD:
        prediction_text = "Cervix Detected"
    else:
        prediction_text = "Non-Cervix"

    scores = {
        "Cervix":     round(prob_cervix, 4),
        "Non-Cervix": round(prob_non_cervix, 4),
    }

    return scores, prediction_text


# ------------------------------------
# GRADIO USER INTERFACE
# ------------------------------------

with gr.Blocks(theme=gr.themes.Soft()) as app:

    gr.Markdown("""
    # Gatekeeper Model
    ### Cervix Image Binary Classifier
    Upload an image to classify it as Cervix or Non-Cervix
    ---
    """)

    with gr.Row():

        with gr.Column():
            input_image = gr.Image(
                label="Upload Image",
                type="numpy"
            )
            classify_btn = gr.Button(
                "Run Classification",
                variant="primary",
                size="lg"
            )
            clear_btn = gr.Button(
                "Clear",
                variant="secondary",
                size="sm"
            )

        with gr.Column():
            output_scores = gr.Label(
                label="Confidence Scores",
                num_top_classes=2
            )
            output_text = gr.Textbox(
                label="Prediction",
                interactive=False,
                text_align="center"
            )

    gr.Markdown("""
    ---
    | Index | Label       | Meaning                          |
    |-------|-------------|----------------------------------|
    | 0     | Non-Cervix  | Image does NOT contain cervix    |
    | 1     | Cervix      | Image contains cervix            |

    ---
    Disclaimer: This tool is for research purposes only.
    It is not intended for clinical diagnosis or medical use.
    """)

    classify_btn.click(
        fn=classify_image,
        inputs=input_image,
        outputs=[output_scores, output_text]
    )

    clear_btn.click(
        fn=lambda: (None, None, ""),
        inputs=None,
        outputs=[input_image, output_scores, output_text]
    )

app.launch()