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app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+import requests
+from io import BytesIO
+
+# Load the pre-trained chest X-ray classification model
+try:
+    model = load_model('D:/1-brain_insipred/cv/chest_xray/models/chest_xray_model.h5')
+except Exception as e:
+    st.error("Error loading model")
+    st.stop()
+
+# Define the class names for prediction output
+class_names = ['Normal', 'Pneumonia']
+
+# Prediction class for encapsulating the prediction logic
+class Prediction:
+    def __init__(self, model):
+        self.model = model
+
+    def classify_image(self, image):
+        try:
+            image = Image.fromarray(image).convert('RGB')
+            image = image.resize((512, 512))
+            image_array = np.array(image).astype(np.float32) / 255.0
+            image_array = np.expand_dims(image_array, axis=0)
+
+            predictions = self.model.predict(image_array)[0]
+            predicted_class_idx = np.argmax(predictions)
+            predicted_class = class_names[predicted_class_idx]
+            predicted_confidence = predictions[predicted_class_idx] * 100
+
+            return predicted_class, predicted_confidence, predictions
+        except Exception as e:
+            st.error("Error during classification")
+            return None, None, None
+
+# Initialize the Prediction class
+predictor = Prediction(model)
+
+# Streamlit app layout
+st.title("📊 Chest X-Ray Classification")
+st.markdown("""
+    Upload one or more chest X-ray images or provide an image URL, and the model will classify each as either **Normal** or **Pneumonia**.
+""")
+
+# Input option selection
+input_option = st.radio("Choose how to upload the image(s):", ("Upload Image(s)", "Image URL"))
+
+# Initialize images list
+images = []
+
+# Patient name input
+patient_name = st.text_input("### Patient Name")
+
+if input_option == "Upload Image(s)":
+    uploaded_images = st.file_uploader("### Step 1: Upload Your Chest X-Ray Image(s)", type=["jpg", "jpeg", "png"],
+                                       accept_multiple_files=True)
+    if uploaded_images:
+        for uploaded_image in uploaded_images:
+            try:
+                image = np.array(Image.open(uploaded_image))
+                images.append((image, uploaded_image.name))
+            except Exception as e:
+                st.error("Error loading image")
+
+elif input_option == "Image URL":
+    image_url = st.text_input("### Step 1: Enter the Image URL")
+    if image_url:
+        try:
+            response = requests.get(image_url)
+            if response.status_code == 200:
+                images.append((np.array(Image.open(BytesIO(response.content))), image_url))
+                st.markdown(f"[Image URL]({image_url})")
+            else:
+                st.error("Error fetching image from URL: Unable to retrieve the image.")
+        except Exception as e:
+            st.error("Error fetching image from URL")
+
+# Store classification results
+results = []
+
+if images:
+    submit_button = st.button("Submit", key="submit")
+    if submit_button:
+        st.write("### Step 2: Review the Uploaded Image(s) and Results")
+
+        for idx, (image, image_name) in enumerate(images):
+            patient_display_name = f"[{patient_name}](#{image_name})"
+            st.write(f"#### Patient: {patient_display_name}")
+
+            col1, col2 = st.columns([2, 1])
+            with col1:
+                st.image(image, caption=image_name, use_column_width=True, clamp=True)
+
+            with col2:
+                st.subheader("Prediction Results")
+                with st.spinner("Processing..."):
+                    predicted_class, predicted_confidence, predictions = predictor.classify_image(image)
+
+                if predicted_class is not None:
+                    st.markdown(f"""
+                    <div style="border: 2px solid #2196F3; padding: 10px; border-radius: 5px;">
+                        <p style="font-size: 16px; font-weight: bold;">Predicted Class: {predicted_class}</p>
+                        <p style="font-size: 16px;">Confidence: {predicted_confidence:.2f}%</p>
+                        <p style="font-size: 16px; font-weight: bold;">Class Confidence Levels:</p>
+                        <ul style="list-style-type: none; padding: 0;">
+                            <li style="color: #4CAF50;">Normal: {predictions[0] * 100:.1f}%</li>
+                            <li style="color: #F44336;">Pneumonia: {predictions[1] * 100:.1f}%</li>
+                        </ul>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+                    results.append((idx + 1, patient_name, predicted_class, predicted_confidence, predictions))
+
+                    st.markdown("---")
+
+        # Button to manually start a new session
+        if st.button("Start New Session"):
+            images.clear()
+            st.experimental_rerun()  # Rerun the app to refresh the state
+
+st.write("### Additional Information")
+st.markdown("""
+    - This model is trained to differentiate between normal and pneumonia-affected chest X-rays.
+    - Confidence levels are displayed as a percentage for each class.
+""")

chest_xray_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce61eb31b48a0bdbb7e25d05329a26d4f4ac323a60723f09b8273cd0791431ea
+size 28994168