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import gradio as gr
from keras.models import load_model
from huggingface_hub import hf_hub_download
from PIL import Image
import numpy as np
# -------------------------------
# MODEL LOADING
# -------------------------------
MODEL_PATH = hf_hub_download(
repo_id="aadityaramrame/blood-cell-cancer-detector",
filename="cancer_classifier.h5"
)
model = load_model(MODEL_PATH)
# Class mapping
CLASSES = [
"platelet",
"monocyte",
"lymphocyte",
"erythroblast",
"eosinophil",
"basophil"
]
# -------------------------------
# PREDICTION FUNCTION
# -------------------------------
def classify_cancer(image):
try:
image = image.convert("RGB").resize((224, 224))
img_array = np.expand_dims(np.array(image) / 255.0, axis=0)
prediction = model.predict(img_array)
predicted_class = int(np.argmax(prediction))
confidence = float(np.max(prediction))
label = CLASSES[predicted_class]
return f"🧫 **Predicted Cell Type:** {label}\n📊 **Confidence:** {confidence:.3f}"
except Exception as e:
return f"⚠️ Error: {str(e)}"
# -------------------------------
# GRADIO INTERFACE
# -------------------------------
demo = gr.Interface(
fn=classify_cancer,
inputs=gr.Image(type="pil", label="📸 Upload Blood Cell Image"),
outputs=gr.Markdown(label="Result"),
title="🧬 Blood Cell Cancer Detection",
description=(
"Upload a blood cell image to classify its type using a trained CNN model.\n"
"Model trained on microscopic blood cell images for cancer detection."
),
theme="soft"
)
# -------------------------------
# LAUNCH
# -------------------------------
if __name__ == "__main__":
demo.launch()
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