Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,129 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import io
 
-"""
-# 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])),
-    ))
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="YOLO Object Detection",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# --- Custom CSS for Styling ---
+st.markdown("""
+<style>
+    /* Main app background */
+    .stApp {
+        background-color: #f0f2f6;
+    }
+    /* Sidebar styling */
+    .css-1d391kg {
+        background-color: #ffffff;
+    }
+    /* Title styling */
+    h1 {
+        color: #1E3A8A; /* A deep blue color */
+        text-align: center;
+    }
+    /* Header styling */
+    h2, h3 {
+        color: #3B82F6; /* A lighter blue */
+    }
+    /* Expander styling */
+    .st-expander {
+        border: 1px solid #ddd;
+        border-radius: 10px;
+        background-color: #ffffff;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# --- Model Loading ---
+# Use st.cache_resource to load the model only once
+@st.cache_resource
+def load_model(model_path):
+    """
+    Loads the YOLO model from the specified path.
+    Caches the model to avoid reloading on every interaction.
+    """
+    try:
+        model = YOLO(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+# Path to your custom model
+MODEL_PATH = "rssi_last.pt"
+model = load_model(MODEL_PATH)
+
+# --- Sidebar ---
+st.sidebar.header("Configuration")
+confidence_threshold = st.sidebar.slider(
+    "Confidence Threshold", 0.0, 1.0, 0.4, 0.05
+)
+st.sidebar.markdown("---")
+uploaded_file = st.sidebar.file_uploader(
+    "Upload an image...", type=["jpg", "jpeg", "png"]
+)
+
+st.sidebar.markdown("---")
+st.sidebar.markdown(
+    "**About this App**\n\n"
+    "This application uses a custom-trained YOLO model to detect objects in images. "
+    "Upload an image and see the magic!"
+)
+
+# --- Main Page ---
+st.title("🖼️ Custom Object Detection with YOLO")
+st.write("Upload an image via the sidebar to see the model's predictions.")
+
+if uploaded_file is not None:
+    # Read the uploaded image file
+    image_data = uploaded_file.getvalue()
+    original_image = Image.open(io.BytesIO(image_data))
+    
+    # Create two columns for side-by-side display
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("Original Image")
+        st.image(original_image, caption="Your uploaded image.", use_column_width=True)
+
+    if model:
+        # Perform inference
+        with st.spinner("Running detection..."):
+            results = model(original_image, conf=confidence_threshold)
+        
+        # The result object contains the annotated image and detection data
+        result = results[0]
+        
+        # Use the plot() method to get an annotated image (in BGR format)
+        annotated_image_bgr = result.plot()
+        # Convert BGR to RGB for display in Streamlit
+        annotated_image_rgb = annotated_image_bgr[..., ::-1]
+
+        with col2:
+            st.subheader("Detected Objects")
+            st.image(annotated_image_rgb, caption="Image with detected objects.", use_column_width=True)
+
+        # Display detection details
+        st.subheader("Detection Details")
+        if len(result.boxes) > 0:
+            with st.expander("Click to see detailed results", expanded=True):
+                # Extract details for each detected box
+                for i, box in enumerate(result.boxes):
+                    label = result.names[box.cls[0].item()]
+                    conf = box.conf[0].item()
+                    xywhn = box.xywhn[0].tolist() # Normalized xywh
+                    
+                    st.markdown(f"**Object {i+1}: `{label}`**")
+                    st.write(f"- Confidence: **{conf:.2f}**")
+                    st.write(f"- Bounding Box (Normalized xywh):")
+                    st.code(f"  x: {xywhn[0]:.4f}, y: {xywhn[1]:.4f}, width: {xywhn[2]:.4f}, height: {xywhn[3]:.4f}")
+        else:
+            st.info("No objects were detected with the current confidence threshold.")
+else:
+    st.info("Please upload an image using the sidebar to begin.")