Spaces:
Sleeping
Sleeping
| import os | |
| os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" | |
| import streamlit as st | |
| # Force immediate render on Hugging Face | |
| st.set_page_config(page_title="COVID-19 AI System", layout="centered") | |
| st.title("🧠 COVID-19 AI Dashboard") | |
| st.write("Initializing app... Please use the sidebar to select a module.") | |
| # Now import other things | |
| 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 ------------------------- | |
| # ------------------------- 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..."): | |
| try: | |
| # ✅ Load model from local models/ folder (no download, no 403 error) | |
| model = load_model("models/covid_xray_model.keras") | |
| st.success("✅ Model loaded successfully!") | |
| # ✅ Process image and predict | |
| 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}`") | |
| except Exception as e: | |
| st.error(f"🚫 Prediction failed. Error: `{e}`") | |
| # ------------------------- 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)) | |