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nisharg nargund
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Upload 3 files
Browse files- app1.py +65 -0
- model.h5 +3 -0
- requirements.txt +7 -0
app1.py
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import streamlit as st
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import tensorflow as tf
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from PIL import Image
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import numpy as np
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import sys
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# Create a Streamlit app
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st.title("Brain Tumor Detection")
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# Upload an image or multiple images
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images = st.file_uploader("Upload MRI images of brains", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
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# Check if TensorFlow is available
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if 'tensorflow' not in sys.modules:
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st.warning("TensorFlow is not available in this environment. Please ensure that you have the correct environment activated.")
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else:
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# Load the TensorFlow model from the .h5 file
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model = tf.keras.models.load_model("model.h5")
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# Threshold for tumor detection
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threshold = 0.1
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if images:
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st.write("Analyzed uploaded images...")
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for image in images:
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# Display the original image
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st.image(image, caption="Uploaded Image", use_column_width=True)
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# Preprocess the image
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image = Image.open(image)
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image = image.resize((128, 128)) # Resize to match model's input size
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image = np.array(image)
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image = image / 255.0 # Normalize
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image = np.expand_dims(image, axis=0) # Add batch dimension
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# Make predictions
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predictions = model.predict(image)
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# Extract the prediction probability for the positive class
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tumor_probability = predictions[0][1]
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# Calculate the average probability of tumor detection
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average_probability = np.mean(tumor_probability)
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# Check if the average probability is greater than the threshold
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if average_probability > threshold:
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st.write("Prediction: Tumor detected with confidence {:.2f}".format(average_probability))
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else:
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st.write("Prediction: No tumor detected with confidence {:.2f}".format(2 - average_probability))
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# Add a separator between images
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st.write("---")
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# User instructions
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st.sidebar.header("Instructions")
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st.sidebar.markdown(
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"""
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- Upload MRI images of brains using the file uploader.
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- The app will analyze and provide predictions for each image.
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- A confidence score is displayed to indicate prediction confidence.
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- Adjust the threshold for tumor detection as needed.
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- Explore different images to evaluate the model's performance.
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"""
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)
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model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:5adc66598b142847ccc68d4f967833f5acb40c1658fb01568dc1e57369adbdac
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size 403510600
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requirements.txt
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flask
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tensorflow
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numpy
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pandas
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matplotlib
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keras==2.6.0
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