Upload streamlit.py

streamlit.py

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+import streamlit as st
+import os
+import requests
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+
+# 🔧 Configure the Streamlit page
+st.set_page_config(page_title="Leukemia Subtype Detection", layout="centered")
+st.markdown("<h1 style='text-align: center; color: #d6336c;'>🎗️ Leukemia Subtype Detection</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center; font-size:18px;'>Upload a blood smear image and detect the leukemia subtype using deep learning models.</p>", unsafe_allow_html=True)
+
+# 🔗 Hugging Face Base URL (confirmed valid)
+HF_BASE = "https://huggingface.co/GarimaSharma75/Leukemia_Subtype/resolve/main/"
+SAVE_DIR = "models"
+
+# 📌 Class labels
+CLASS_NAMES = ["Early Pre-B ALL", "Pre-B ALL", "Pro-B ALL", "Healthy"]
+
+# 🔢 Model files hosted on Hugging Face
+model_files = {
+    "DenseNet121": "DenseNet121_model.keras",
+    "MobileNetV2": "MobileNetV2_model.keras",
+    "VGG16": "VGG16_model.keras",
+    "CustomCNN": "CustomCNN_model.keras"
+}
+
+# 📥 Download the model file from Hugging Face if it's not saved locally
+def download_if_not_exists(filename):
+    os.makedirs(SAVE_DIR, exist_ok=True)
+    filepath = os.path.join(SAVE_DIR, filename)
+    if not os.path.exists(filepath):
+        url = HF_BASE + filename
+        with st.spinner(f"📥 Downloading `{filename}`..."):
+            response = requests.get(url)
+            if response.status_code == 200:
+                with open(filepath, "wb") as f:
+                    f.write(response.content)
+            else:
+                st.error(f"❌ Failed to download {filename} from Hugging Face.")
+                st.stop()
+    return filepath
+
+# 🧼 Image preprocessing
+def preprocess(img):
+    img = img.resize((224, 224))
+    img_array = np.array(img) / 255.0
+    return np.expand_dims(img_array, axis=0)
+
+# 📍 Sidebar: Model selection and info
+with st.sidebar:
+    st.markdown("## 🧠 Select Model")
+    selected_model = st.selectbox("Choose one model to run", list(model_files.keys()))
+    st.markdown("### ℹ️ About the Models")
+    st.info("""
+    • **DenseNet121** – Deep CNN with dense connections  
+    • **MobileNetV2** – Lightweight CNN  
+    • **VGG16** – Classic 16-layer CNN  
+    • **CustomCNN** – Custom-built architecture
+    """)
+    st.markdown("---")
+    st.markdown("Made by **Garima Sharma** 💖")
+
+# 📤 Upload image
+uploaded_file = st.file_uploader("📤 Upload a blood smear image (JPG/PNG)", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    img = Image.open(uploaded_file).convert("RGB")
+    st.image(img, caption="Uploaded Image", use_container_width=True)
+
+    if st.button("🔍 Run Detection"):
+        with st.spinner("⏳ Please wait while the model is downloading and predicting..."):
+            input_data = preprocess(img)
+            model_path = download_if_not_exists(model_files[selected_model])
+            model = load_model(model_path)
+            preds = model.predict(input_data)
+            pred_class = CLASS_NAMES[np.argmax(preds)]
+            prob = np.max(preds) * 100
+
+        st.success(f"✅ **{selected_model}** predicts: **{pred_class}** with `{prob:.2f}%` confidence")
+else:
+    st.warning("📎 Please upload an image to get started.")