Rename src/streamlit_app.py to src/app.py

src/app.py

@@ -0,0 +1,74 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.utils import load_img, img_to_array
+from io import BytesIO
+
+st.set_page_config(page_title="Forest Fire Detection", page_icon="🔥", layout="centered")
+
+# Custom header with emoji
+st.markdown(
+    """
+    <div style='text-align: center; margin-bottom: 20px;'>
+        <h1>🔥 Forest Fire Detection Demo 🔥</h1>
+        <p style='font-size:20px;'>Upload a forest image and let AI detect fire!<br>
+        <span style='font-size:40px;'>🌲🌳🌴</span></p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+st.sidebar.title("About")
+st.sidebar.info(
+    "Upload a forest image to detect fire using a deep learning model. "
+    "This demo is powered by TensorFlow and Streamlit."
+)
+
+# Load model only once
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('FFD.keras')
+model = load_model()
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    with st.spinner("Analyzing image..."):
+        img = load_img(BytesIO(uploaded_file.read()), target_size=(150, 150))
+        img_array = img_to_array(img) / 255.0
+        img_array = img_array.reshape(1, 150, 150, 3)
+        prediction = model.predict(img_array)
+        confidence = float(prediction[0][0])
+        result = 'Fire Detected' if confidence > 0.5 else 'No Fire'
+
+        # Improved two-column layout with centered content and card-style result
+        col1, col2 = st.columns([1.2, 1])
+        with col1:
+            st.markdown("<div style='display:flex;justify-content:center;'>", unsafe_allow_html=True)
+            st.image(img, caption="Uploaded Image", width=260)
+            st.markdown("</div>", unsafe_allow_html=True)
+        with col2:
+            st.markdown(
+                f"""
+                <div style='background: rgb(38, 39, 48); border-radius: 16px; box-shadow: 0 2px 12px rgba(0,0,0,0.08); padding: 24px; text-align: center; margin-top: 20px;'>
+                    <h2 style='color:{"red" if result=="Fire Detected" else "green"}; margin-bottom: 10px;'>
+                        {"🔥" if result=="Fire Detected" else "🌲"} {result}
+                    </h2>
+                    <div style='margin-bottom: 10px;'>
+                        <progress value='{confidence:.2f}' max='1' style='width:80%; height:18px;'></progress>
+                    </div>
+                    <div style='font-size:18px; margin-bottom: 10px;'><b>Confidence:</b> {confidence:.2f}</div>
+                </div>
+                """,
+                unsafe_allow_html=True
+            )
+    st.markdown("<br>", unsafe_allow_html=True)
+else:
+    st.info("Please upload an image to get started.")
+
+# Footer
+st.markdown(
+    "<hr><div style='text-align:center;font-size:14px;'>"
+    "Made with ❤️ by CoderKP using Streamlit & TensorFlow"
+    "</div>",
+    unsafe_allow_html=True
+)

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))