Update app.py

app.py

@@ -1,83 +1,82 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-import matplotlib.pyplot as plt
-
-# ------------------------- PAGE CONFIG -------------------------
-st.set_page_config(page_title="COVID-19 AI System", layout="centered")
-
-# ------------------------- SIDEBAR -------------------------
-st.sidebar.image("https://i.imgur.com/NbA3RfI.png", use_container_width=True)  # Optional: Replace with your own logo
-st.sidebar.title("🧠 COVID-19 AI Dashboard")
-app_mode = st.sidebar.radio("📍 Select Module", ["🏠 Home", "🩻 X-ray Classifier", "📊 Global Data Analysis"])
-
-# ------------------------- CUSTOM CSS -------------------------
-st.markdown("""
-    <style>
-        .main { background-color: #f7f7f7; padding: 20px; border-radius: 10px; }
-        .title { text-align: center; font-size: 36px; color: #4A90E2; font-weight: bold; }
-        .subtitle { text-align: center; font-size: 18px; color: #444; }
-    </style>
-""", unsafe_allow_html=True)
-
-# ------------------------- HOME PAGE -------------------------
-if app_mode == "🏠 Home":
-    st.markdown('<div class="main"><div class="title">COVID-19 Detection & Analysis</div><div class="subtitle">An integrated AI system using Deep Learning and WHO data</div></div>', unsafe_allow_html=True)
-    st.write("Welcome to the COVID-19 AI dashboard. Use the sidebar to navigate between modules:")
-
-    st.markdown("""
-    - 🩻 **X-ray Classifier**: Upload a chest X-ray to detect COVID-19 using a deep learning model.
-    - 📊 **Global Data Analysis**: Explore real-world trends using WHO global COVID-19 dataset.
-    """)
-
-# ------------------------- X-RAY PREDICTION -------------------------
-elif app_mode == "🩻 X-ray Classifier":
-    st.header("🩺 Chest X-ray COVID Prediction")
-
-    uploaded_image = st.file_uploader("📤 Upload Chest X-ray", type=["jpg", "jpeg", "png"])
-
-    if uploaded_image:
-        with st.spinner("🔍 Predicting..."):
-            model = load_model("covid_xray_model.keras")
-            img = image.load_img(uploaded_image, target_size=(224, 224))
-            img_array = image.img_to_array(img) / 255.0
-            img_array = np.expand_dims(img_array, axis=0)
-
-            pred = model.predict(img_array)[0]
-            labels = ['COVID', 'NORMAL', 'Viral Pneumonia']
-            result = labels[np.argmax(pred)]
-
-            col1, col2 = st.columns([1, 2])
-            with col1:
-                st.image(uploaded_image, caption="Uploaded X-ray", use_container_width=True)
-            with col2:
-                st.success(f"🧠 **Predicted Condition:** `{result}`")
-
-# ------------------------- DATA ANALYSIS -------------------------
-elif app_mode == "📊 Global Data Analysis":
-    st.header("📊 WHO COVID-19 Data Analysis")
-
-    df = pd.read_csv("WHO-COVID-19-global-data.csv")
-    df["Date_reported"] = pd.to_datetime(df["Date_reported"])
-
-    top_countries = df.groupby("Country")["New_cases"].sum().sort_values(ascending=False).head(10)
-
-    st.subheader("🌍 Top 10 Countries by Total Reported Cases")
-    st.bar_chart(top_countries)
-
-    st.subheader("📈 Trend for Selected Country")
-    selected_country = st.selectbox("Choose a country", df["Country"].unique())
-    country_data = df[df["Country"] == selected_country]
-
-    col1, col2 = st.columns(2)
-    with col1:
-        st.markdown("**Daily New Cases**")
-        st.line_chart(country_data.set_index("Date_reported")[["New_cases"]])
-    with col2:
-        st.markdown("**Cumulative Cases Over Time**")
-        st.line_chart(country_data.set_index("Date_reported")[["Cumulative_cases"]])
-
-    with st.expander("📄 Show Raw Data"):
-        st.dataframe(country_data.tail(10))
+import streamlit as st
+import pandas as pd
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import matplotlib.pyplot as plt
+
+# ------------------------- PAGE CONFIG -------------------------
+st.set_page_config(page_title="COVID-19 AI System", layout="centered")
+
+# ------------------------- SIDEBAR -------------------------
+st.sidebar.title("🧠 COVID-19 AI Dashboard")
+app_mode = st.sidebar.radio("📍 Select Module", ["🏠 Home", "🩻 X-ray Classifier", "📊 Global Data Analysis"])
+
+# ------------------------- CUSTOM CSS -------------------------
+st.markdown("""
+    <style>
+        .main { background-color: #f7f7f7; padding: 20px; border-radius: 10px; }
+        .title { text-align: center; font-size: 36px; color: #4A90E2; font-weight: bold; }
+        .subtitle { text-align: center; font-size: 18px; color: #444; }
+    </style>
+""", unsafe_allow_html=True)
+
+# ------------------------- HOME PAGE -------------------------
+if app_mode == "🏠 Home":
+    st.markdown('<div class="main"><div class="title">COVID-19 Detection & Analysis</div><div class="subtitle">An integrated AI system using Deep Learning and WHO data</div></div>', unsafe_allow_html=True)
+    st.write("Welcome to the COVID-19 AI dashboard. Use the sidebar to navigate between modules:")
+
+    st.markdown("""
+    - 🩻 **X-ray Classifier**: Upload a chest X-ray to detect COVID-19 using a deep learning model.
+    - 📊 **Global Data Analysis**: Explore real-world trends using WHO global COVID-19 dataset.
+    """)
+
+# ------------------------- X-RAY PREDICTION -------------------------
+elif app_mode == "🩻 X-ray Classifier":
+    st.header("🩺 Chest X-ray COVID Prediction")
+
+    uploaded_image = st.file_uploader("📤 Upload Chest X-ray", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image:
+        with st.spinner("🔍 Predicting..."):
+            model = load_model("covid_xray_model.keras")
+            img = image.load_img(uploaded_image, target_size=(224, 224))
+            img_array = image.img_to_array(img) / 255.0
+            img_array = np.expand_dims(img_array, axis=0)
+
+            pred = model.predict(img_array)[0]
+            labels = ['COVID', 'NORMAL', 'Viral Pneumonia']
+            result = labels[np.argmax(pred)]
+
+            col1, col2 = st.columns([1, 2])
+            with col1:
+                st.image(uploaded_image, caption="Uploaded X-ray", use_container_width=True)
+            with col2:
+                st.success(f"🧠 **Predicted Condition:** `{result}`")
+
+# ------------------------- DATA ANALYSIS -------------------------
+elif app_mode == "📊 Global Data Analysis":
+    st.header("📊 WHO COVID-19 Data Analysis")
+
+    df = pd.read_csv("WHO-COVID-19-global-data.csv")
+    df["Date_reported"] = pd.to_datetime(df["Date_reported"])
+
+    top_countries = df.groupby("Country")["New_cases"].sum().sort_values(ascending=False).head(10)
+
+    st.subheader("🌍 Top 10 Countries by Total Reported Cases")
+    st.bar_chart(top_countries)
+
+    st.subheader("📈 Trend for Selected Country")
+    selected_country = st.selectbox("Choose a country", df["Country"].unique())
+    country_data = df[df["Country"] == selected_country]
+
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown("**Daily New Cases**")
+        st.line_chart(country_data.set_index("Date_reported")[["New_cases"]])
+    with col2:
+        st.markdown("**Cumulative Cases Over Time**")
+        st.line_chart(country_data.set_index("Date_reported")[["Cumulative_cases"]])
+
+    with st.expander("📄 Show Raw Data"):
+        st.dataframe(country_data.tail(10))