Added Streamlit and Model file

app.py

@@ -1,4 +1,60 @@
 import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load the pre-trained Keras model for lung cancer classification
+model = load_model("./model/lung_cancer_detection_model.h5", compile=False)
+
+# Then, compile your model using the optimizer
+model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
+
+
+# Function to preprocess the uploaded image
+def predict_single_image(image_path, model, target_size=(128, 128)):
+    # Load and preprocess the image
+    img = image.load_img(image_path, target_size=target_size, color_mode="grayscale")
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img /= 255.0  # Rescale the image
+
+    # Predict the class probabilities
+    probabilities = model.predict(img)
+    # display(probabilities)
+
+    # Determine the predicted class label
+    predicted_class = "positive" if probabilities[0][0] > 0.5 else "negative"
+
+    return predicted_class, probabilities[0][0]
+
+
+# Function to classify the uploaded image
+def classify_lung_cancer(img):
+    # Call the function to predict the class label for the single image
+    predicted_label, confidence = predict_single_image(
+        img, model, target_size=(512, 512)
+    )
+
+    # Print the prediction
+    # print('Predicted Label:', predicted_label)
+    # print('Confidence:', confidence)
+    return f"Prediction: {predicted_label}\n(Confidence: {confidence:.2f})"
+
+
+# Streamlit app
+st.title("Lung Cancer Classification")
+st.write(
+    "Upload an image and the model will classify it as positive or negative for lung cancer."
+)
+
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    st.image(uploaded_image, caption="Uploaded Image.", use_column_width=True)
+
+    # Classify the uploaded image
+    if st.button("Classify"):
+        predicted_label, confidence = classify_lung_cancer(uploaded_image)
+        st.write(f"Prediction: {predicted_label}")
+        st.write(f"Confidence: {confidence:.2f}")

model/lung_cancer_detection_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:384d3ed0f01155e64ffabf362d5df13fc425c22a1de8696c0976090adb04882b
+size 710743384