Upload main files

app.py

@@ -0,0 +1,63 @@
+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+# Load the model
+model = load_model('cnn_model.h5')
+
+# Function to process the uploaded image
+def process_image(img):
+    img = img.convert("RGB")  # Convert to RGB
+    img = img.resize((64, 64))  # Resize to match model input
+    img = np.array(img) / 255.0  # Normalize pixel values
+    img = np.expand_dims(img, axis=0)  # Expand dimensions for batch processing
+    return img
+
+# Streamlit App
+st.set_page_config(page_title="Pneumonia Detector", page_icon="🩺", layout="centered")
+
+# Apply custom styling
+st.markdown(
+    """
+    <style>
+        .main {background-color: #e3f2fd;}  /* Light blue background */
+        h1 {color: #008080; text-align: center;}  /* Teal color title */
+        .stButton button {background-color: #008080; color: white; font-size: 18px;} /* Stylish button */
+        .result-box {padding: 10px; border-radius: 10px; font-size: 20px; text-align: center; font-weight: bold;}
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Title
+st.title("🩺 Pneumonia Detection System")
+st.write("Upload a chest X-ray image to check if it indicates Pneumonia or is Normal.")
+
+# File uploader
+file = st.file_uploader("📷 Upload an Image", type=["jpg", "jpeg", "png"])
+
+if file is not None:
+    # Display uploaded image
+    img = Image.open(file)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    # Predict button
+    if st.button("🔍 Predict"):
+        with st.spinner("Analyzing... ⏳"):
+            image = process_image(img)
+            predictions = model.predict(image)
+            predicted_class = np.argmax(predictions)
+
+            # Class names
+            class_names = ["NORMAL", "PNEUMONIA"]
+            result_text = class_names[predicted_class]
+
+            # Dynamic coloring for results
+            color = "green" if predicted_class == 0 else "red"
+
+            # Display result
+            st.markdown(
+                f'<div class="result-box" style="background-color: {color}; color: white;">{result_text}</div>',
+                unsafe_allow_html=True,
+            )

cnn_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29d5f4037741df724d8ff85cf8d1d4781f3042dc6b1a26b6f861359002ad060d
+size 134058032

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+tensorflow
+pillow