app.py

import streamlit as st
import os
import tempfile
import cv2
import av
import torch
import torch.nn as nn
from PIL import Image
from ultralytics import YOLO
from torchvision import models, transforms
from streamlit_webrtc import webrtc_streamer, VideoProcessorBase

# =====================================================
# PAGE CONFIG
# =====================================================
st.set_page_config(page_title="SmartVision AI", layout="centered")
st.title("🚀 SmartVision AI")

# =====================================================
# LOCAL MODEL PATHS
# =====================================================
VGG16_PATH = "VGG16_best.pth"  # Local path
YOLO_PATH = "best.pt"          # Local path

# =====================================================
# LOAD MODELS
# =====================================================
@st.cache_resource
def load_yolo():
    model = YOLO(YOLO_PATH)  # Local file path
    return model

@st.cache_resource
def load_vgg16():
    class_names = [
        'airplane','bed','bench','bicycle','bird','bottle','bowl','bus','cake',
        'car','cat','chair','couch','cow','cup','dog','elephant','horse',
        'motorcycle','person','pizza','potted plant','stop sign',
        'traffic light','truck'
    ]

    model = models.vgg16(pretrained=False)
    model.classifier[6] = nn.Linear(4096, len(class_names))
    model.load_state_dict(torch.load(VGG16_PATH, map_location="cpu"))
    model.eval()

    return model, class_names

yolo_model = load_yolo()
vgg_model, CLASS_NAMES = load_vgg16()

# =====================================================
# TABS (2 PAGE APP)
# =====================================================
tab1, tab2 = st.tabs(["🔍 Object Detection", "🧠 Image Classification"])

# =====================================================
# 🔍 OBJECT DETECTION PAGE
# =====================================================
with tab1:
    st.header("🔍 Object Detection (YOLO)")

    mode = st.radio("Select Mode", ["📁 Image Upload", "📷 Webcam"])

    if mode == "📁 Image Upload":
        img_file = st.file_uploader("Upload Image", type=["jpg","jpeg","png"],key="detector_uploader")

        if img_file:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
                tmp.write(img_file.read())
                img_path = tmp.name

            results = yolo_model(img_path, conf=0.4)
            annotated = results[0].plot()
            annotated = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)

            st.image(annotated, caption="Detected Objects", use_container_width=True)
            st.success(f"Objects Detected: {len(results[0].boxes)}")

    else:
        class YOLOProcessor(VideoProcessorBase):
            def recv(self, frame):
                img = frame.to_ndarray(format="bgr24")
                results = yolo_model(img, conf=0.4)
                return av.VideoFrame.from_ndarray(results[0].plot(), format="bgr24")

        webrtc_streamer(
            key="yolo-webcam",
            video_processor_factory=YOLOProcessor,
            media_stream_constraints={"video": True, "audio": False},
            async_processing=True
        )

# =====================================================
# 🧠 IMAGE CLASSIFICATION PAGE
# =====================================================
with tab2:
    st.header("🧠 Image Classification Best Model:(VGG16)")

    transform = transforms.Compose([
        transforms.Resize((224,224)),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485,0.456,0.406],
            std=[0.229,0.224,0.225]
        )
    ])

    img_file = st.file_uploader("Upload Image", type=["jpg","jpeg","png"],key="classifier_uploader")

    if img_file:
        image = Image.open(img_file).convert("RGB")
        st.image(image, use_container_width=True)

        tensor = transform(image).unsqueeze(0)

        with torch.no_grad():
            output = vgg_model(tensor)
            probs = torch.softmax(output, dim=1)
            conf, pred = torch.max(probs, 1)

        st.success(
            f"### 🧠 Prediction: **{CLASS_NAMES[pred.item()]}**\n"
            f"### 🎯 Confidence: **{conf.item()*100:.2f}%**"
        )