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LICENSE

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+MIT License
+
+Copyright (c) 2024 Santosh Chavala
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: BrainTumor
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.33.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# BrainTumor

brain.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b61b61648bbe257a15b84852904838af8743a32fda5073cfa595e141318bbfa
+size 85885376

predictions.py

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+import streamlit as st
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load the pre-trained model
+model = load_model('brain.h5')
+
+# Class labels
+class_labels = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
+
+def load_and_predict(image):
+    # Preprocess the image for prediction
+    image = cv2.resize(image, (150, 150))  # Resize the image to match the input shape of the model
+    image = np.expand_dims(image, axis=0)  # Add an extra dimension for batch size
+
+    # Make predictions
+    predictions = model.predict(image)
+    predicted_class_idx = np.argmax(predictions)
+    predicted_class = class_labels[predicted_class_idx]
+
+    return predicted_class
+
+def main():
+    st.title("Brain Tumor Classifier")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
+        st.image(image, caption="Uploaded Image.", width=200)
+
+        if st.button("Predict"):
+            predicted_class = load_and_predict(image)
+            st.success(f"Predicted Class: {predicted_class}")
+
+if __name__ == "__main__":
+    main()