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2305b98 f70e222 2305b98 94d3010 2305b98 94d3010 2305b98 94d3010 2305b98 94d3010 2305b98 94d3010 2305b98 94d3010 2305b98 94d3010 2305b98 94d3010 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import joblib
import numpy as np
import pandas as pd
import streamlit as st
from PIL import Image#f
from tensorflow.keras.models import load_model
# ---------------- Load CSV ----------------
CSV_PATH = "best_models_summary.csv" # Must be in the same folder
df = pd.read_csv(CSV_PATH)
# ---------------- Streamlit Page Config ----------------
st.set_page_config(
page_title="🔥 Wildfire Classification",
layout="wide",
page_icon="🔥"
)
# ---------------- Custom CSS for Colors ----------------
st.markdown("""
<style>
h1 {
color: #ff6f61;
text-align: center;
}
.stButton>button {
background-color: #ff6f61;
color:white;
font-weight:bold;
}
.stSelectbox>div>div>div>span {
color: #ff6f61;
}
.stFileUploader>div>div>div>label {
font-weight:bold;
}
.stMarkdown p {
font-size:16px;
}
.stImage>div>div>img {
border: 2px solid #ff6f61;
border-radius: 10px;
}
</style>
""", unsafe_allow_html=True)
# ---------------- Header ----------------
st.markdown("<h1>🔥 Wildfire Image Classification 🔥</h1>", unsafe_allow_html=True)
st.markdown("Upload an image, select dataset type and metric priority. The app will automatically select the best model.", unsafe_allow_html=True)
st.markdown("---")
# ---------------- Layout ----------------
col1, col2 = st.columns(2)
with col1:
uploaded_file = st.file_uploader("Upload Image", type=['png','jpg','jpeg'])
with col2:
dataset_type = st.selectbox("Select Dataset Type", ['satellite','uav'])
metric_priority = st.selectbox("Select Metric Priority", ['accuracy','precision','recall','f1_score','best_overall'])
# ---------------- Process Uploaded Image ----------------
if uploaded_file:
img = Image.open(uploaded_file).convert('RGB')
st.image(img, caption='Uploaded Image', use_container_width=True)
# ---------------- Select Model ----------------
subset = df[df['dataset']==dataset_type]
if metric_priority != "best_overall":
subset = subset[subset['metric']==metric_priority]
else:
# Best overall: max f1_score
subset = subset.loc[subset['f1_score'].idxmax():subset['f1_score'].idxmax()+1]
model_row = subset.iloc[0]
model_path = model_row['model_path']
model_type = model_row['model_type']
st.markdown(f"### Using Model: **{model_row['model_name']}** ({model_type})")
# ---------------- Load Model ----------------
model = None
try:
if model_type.lower() in ["h5","keras"]:
model = load_model(model_path)
elif model_type.lower() == "joblib":
model = joblib.load(model_path)
else:
st.error("Unsupported model type!")
except Exception as e:
st.error(f"Error loading model: {e}")
# ---------------- Preprocess Image ----------------
img_array = np.array(img.resize((224,224)))/255.0
img_array_exp = np.expand_dims(img_array, axis=0)
# ---------------- Predict ----------------
pred = None
try:
if model_type.lower() in ['h5','keras']:
pred_probs = model.predict(img_array_exp)
pred_class = np.argmax(pred_probs, axis=1)[0]
pred = pred_class
elif model_type.lower() == 'joblib':
# Flatten image for ML models if needed
pred = model.predict(img_array_exp.reshape(1,-1))[0]
except Exception as e:
st.error(f"Error during prediction: {e}")
# ---------------- Display Results ----------------
st.markdown("---")
st.markdown("## Prediction Result")
if pred is not None:
st.success(f"Predicted Class: **{pred}** (class index for now)")
st.info(f"Metric Priority: **{metric_priority}**")
st.write(f"Model Accuracy: {model_row['accuracy']:.4f} | Precision: {model_row['precision']:.4f} | Recall: {model_row['recall']:.4f} | F1 Score: {model_row['f1_score']:.4f}")
else:
st.warning("Prediction could not be made. Check model compatibility.")
# Optional style enhancements
st.markdown("<hr style='border:2px solid #ff6f61'>", unsafe_allow_html=True)
st.balloons() |