app.py

import streamlit as st
from PIL import Image, ImageDraw

from transformers import pipeline

st.set_page_config(
    page_title="Smart Vision Inspector",
    page_icon="👁️",
    layout="wide"
)

st.title("👁️ Smart Vision Inspector")

st.markdown("""
### AI-Powered Computer Vision Application

Upload an image and perform:

- Object Detection
- Image Analysis
- Bounding Box Visualization
- AI-Powered Insights
""")

@st.cache_resource
def load_detector():
    return pipeline(
        task="object-detection",
        model="hustvl/yolos-tiny"
    )

detector = load_detector()

uploaded_file = st.file_uploader(
    "Upload an Image",
    type=["jpg", "jpeg", "png"]
)

if uploaded_file is not None:

    image = Image.open(uploaded_file).convert("RGB")

    st.subheader("Original Image")
    st.image(image, use_container_width=True)

    with st.spinner("Running Object Detection..."):
        detections = detector(image)

    draw = ImageDraw.Draw(image)

    object_counts = {}

    for detection in detections:

        score = float(detection["score"])

        if score < 0.5:
            continue

        label = detection["label"]

        object_counts[label] = (
            object_counts.get(label, 0) + 1
        )

        box = detection["box"]

        xmin = int(box["xmin"])
        ymin = int(box["ymin"])
        xmax = int(box["xmax"])
        ymax = int(box["ymax"])

        draw.rectangle(
            [(xmin, ymin), (xmax, ymax)],
            outline="red",
            width=3
        )

        draw.text(
            (xmin, ymin),
            f"{label} {score:.2f}",
            fill="red"
        )

    st.subheader("Detection Results")
    st.image(image, use_container_width=True)

    st.subheader("Detected Objects")

    if object_counts:
        st.json(object_counts)

        summary = []

        for label, count in object_counts.items():
            summary.append(
                f"{count} {label}"
            )

        st.success(
            "Detected: " + ", ".join(summary)
        )

    else:
        st.warning(
            "No objects detected."
        )

with st.sidebar:

    st.header("Skills Demonstrated")

    st.markdown("""
- Computer Vision
- Object Detection
- Image Processing
- Hugging Face Transformers
- Deep Learning
- Python
- Streamlit
    """)

    st.header("Model")

    st.write(
        "YOLOS Tiny"
    )