Commit

app.py

@@ -0,0 +1,312 @@
+# import streamlit as st
+# from ultralytics import YOLO
+# from PIL import Image
+# import tempfile
+# import cv2
+# import av
+# from streamlit_webrtc import webrtc_streamer, VideoProcessorBase
+# import torch
+# import torch.nn as nn
+# from torchvision import models, transforms
+# from PIL import Image
+# import os
+# from huggingface_hub import hf_hub_download
+
+# cache_dir = "models_cache"
+# os.makedirs(cache_dir, exist_ok=True)
+
+# VGG16_best = hf_hub_download(
+#     repo_id="jgvghf/smartvision",
+#     filename="VGG16_best.pth",
+#     token=st.secrets["HuggingFace_token"],
+#     cache_dir=cache_dir
+# )
+
+# yolo_best = hf_hub_download(
+#     repo_id="jgvghf/smartvision",
+#     filename="best.pt",
+#     token=st.secrets["HuggingFace_token"],
+#     cache_dir=cache_dir
+# )
+
+
+# st.set_page_config(page_title="SmartVision AI", layout="centered")
+# st.title("🔍 SmartVision AI – Object Detection")
+
+# # ---------------- LOAD MODEL ----------------
+# model = YOLO(yolo_best)
+
+# # ---------------- MODE SELECTION ----------------
+# mode = st.radio("Select Mode", ["📁 Image Upload", "📷 Webcam","🧠 Image Classification"])
+
+# # ==================================================
+# # 📁 IMAGE UPLOAD MODE
+# # ==================================================
+# if mode == "📁 Image Upload":
+
+#     uploaded_img = st.file_uploader(
+#         "Upload Image",
+#         type=["jpg", "jpeg", "png"]
+#     )
+
+#     if uploaded_img is not None:
+#         with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+#             tmp.write(uploaded_img.read())
+#             image_path = tmp.name
+
+#         results = model(image_path, conf=0.35)
+#         res = results[0]
+
+#         annotated_img = res.plot()
+#         annotated_img = cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB)
+
+#         st.image(annotated_img, caption="Detected Objects", use_container_width=True)
+
+#         if res.boxes is not None:
+#             st.success(f"Detected Objects: {len(res.boxes)}")
+
+# # ==================================================
+# # 📷 WEBCAM MODE
+# # ==================================================
+# elif mode == "📷 Webcam":
+
+#     class YOLOVideoProcessor(VideoProcessorBase):
+#         def recv(self, frame):
+#             img = frame.to_ndarray(format="bgr24")
+
+#             results = model(img, conf=0.35)
+#             res = results[0]
+
+#             annotated_frame = res.plot()
+#             return av.VideoFrame.from_ndarray(annotated_frame, format="bgr24")
+
+#     webrtc_streamer(
+#         key="yolo-webcam",
+#         video_processor_factory=YOLOVideoProcessor,
+#         media_stream_constraints={
+#             "video": True,
+#             "audio": False
+#         },
+#         async_processing=True
+#     )
+# else:
+    
+
+
+#     # =====================================================
+#     # PAGE CONFIG
+#     # =====================================================
+#     # st.set_page_config(page_title="SmartVision AI - Classification", layout="centered")
+#     st.title("🧠 SmartVision AI – Image Classification (VGG16)")
+    
+#     # =====================================================
+#     # LOAD CLASS NAMES
+#     # =====================================================
+#     # CLASS_DIR = "smartvision_dataset/classification/train"
+#     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']
+#     # class_names = sorted(os.listdir(CLASS_DIR))
+#     NUM_CLASSES = len(class_names)
+    
+#     # =====================================================
+#     # LOAD VGG16 MODEL
+#     # =====================================================
+#     @st.cache_resource
+#     def load_vgg16():
+#        vggmodel = models.vgg16(pretrained=False)
+#        vggmodel.classifier[6] = nn.Linear(4096, NUM_CLASSES)
+
+#        vggmodel.load_state_dict(
+#         torch.load(VGG16_best, map_location="cpu")
+#         )
+
+#        vggmodel.eval()
+#        return vggmodel
+
+#     vggmodel = load_vgg16()
+
+#     # =====================================================
+#     # IMAGE TRANSFORMS
+#     # =====================================================
+#     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]
+#     )
+#     ])
+    
+#     # =====================================================
+#     # IMAGE UPLOAD
+#     # =====================================================
+#     uploaded_img = st.file_uploader(
+#     "📁 Upload an image for classification",
+#     type=["jpg", "jpeg", "png"]
+#     )
+
+#     if uploaded_img:
+
+#       image = Image.open(uploaded_img).convert("RGB")
+#       st.image(image, caption="Uploaded Image", use_container_width=True)
+
+#       input_tensor = transform(image).unsqueeze(0)
+
+#       with torch.no_grad():
+#         outputs = vggmodel(input_tensor)
+#         probs = torch.softmax(outputs, dim=1)
+#         confidence, predicted = torch.max(probs, 1)
+
+#       st.success(
+#         f"🧠 Prediction: **{class_names[predicted.item()]}**\n\n"
+#         f"🎯 Confidence: **{confidence.item()*100:.2f}%**"
+#        )
+
+
+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
+from huggingface_hub import hf_hub_download
+
+# =====================================================
+# PAGE CONFIG
+# =====================================================
+st.set_page_config(page_title="SmartVision AI", layout="centered")
+st.title("🚀 SmartVision AI")
+
+# =====================================================
+# DOWNLOAD MODELS (HF HUB)
+# =====================================================
+@st.cache_resource
+def download_models():
+    cache_dir = "models_cache"
+    os.makedirs(cache_dir, exist_ok=True)
+
+    vgg_path = hf_hub_download(
+        repo_id="jgvghf/smartvision",
+        filename="VGG16_best.pth",
+        token=st.secrets["HuggingFace_token"],
+        cache_dir=cache_dir
+    )
+
+    yolo_path = hf_hub_download(
+        repo_id="jgvghf/smartvision",
+        filename="best.pt",
+        token=st.secrets["HuggingFace_token"],
+        cache_dir=cache_dir
+    )
+
+    return vgg_path, yolo_path
+
+VGG16_PATH, YOLO_PATH = download_models()
+
+# =====================================================
+# LOAD MODELS
+# =====================================================
+@st.cache_resource
+def load_yolo():
+    model = YOLO(YOLO_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 (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}%**"
+        )

requirements.txt

@@ -0,0 +1,15 @@
+streamlit
+torch
+torchvision
+ultralytics
+opencv-python
+pillow
+numpy
+matplotlib
+scikit-learn
+datasets
+tqdm
+seaborn
+av
+streamlit_webrtc
+huggingface_hub