deploy app

app.py

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+import streamlit as st
+from PIL import Image, ImageDraw
+import torch
+import numpy as np
+from torchvision import transforms, models
+from ultralytics import YOLO
+from transformers import pipeline
+import tempfile
+from huggingface_hub import hf_hub_download
+
+# ─── 0)class NAMES───_____─────────────
+class_names = [
+    "Barn Swallow", "Bay breasted Warbler", "Black and white Warbler", "Black billed Cuckoo",
+    "Black throated Blue Warbler", "Black throated Sparrow", "Blue Grosbeak", "Blue Jay",
+    "Bobolink", "Bohemian Waxwing", "Bronzed Cowbird", "Brown Creeper",
+    "Brown Pelican", "Brown Thrasher", "Canada Warbler", "Cape Glossy Starling",
+    "Cape May Warbler", "Cardinal", "Carolina Wren", "Caspian Tern",
+    "Cedar Waxwing", "Cerulean Warbler", "Chuck will Widow", "Clark Nutcracker",
+    "Common Yellowthroat", "Eared Grebe", "Eastern Towhee", "European Goldfinch",
+    "Evening Grosbeak", "Forsters Tern", "Fox Sparrow", "Geococcyx",
+    "Golden winged Warbler", "Gray Kingbird", "Gray crowned Rosy Finch", "Green Jay",
+    "Green Violetear", "Green tailed Towhee", "Harris Sparrow", "Heermann Gull",
+    "Hooded Merganser", "Hooded Oriole", "Hooded Warbler", "Horned Grebe",
+    "Horned Lark", "Horned Puffin", "Ivory Gull", "Lazuli Bunting",
+    "Le Conte Sparrow", "Least Auklet", "Loggerhead Shrike", "Magnolia Warbler",
+    "Mallard", "Myrtle Warbler", "Nashville Warbler", "Nelson Sharp tailed Sparrow",
+    "Nighthawk", "Ovenbird", "Pacific Loon", "Painted Bunting",
+    "Palm Warbler", "Parakeet Auklet", "Pied Kingfisher", "Pied billed Grebe",
+    "Pine Grosbeak", "Pine Warbler", "Prairie Warbler", "Prothonotary Warbler",
+    "Purple Finch", "Red bellied Woodpecker", "Red cockaded Woodpecker", "Red eyed Vireo",
+    "Red winged Blackbird", "Rhinoceros Auklet", "Rose breasted Grosbeak", "Ruby throated Hummingbird",
+    "Rufous Hummingbird", "Savannah Sparrow", "Sayornis", "Scarlet Tanager",
+    "Scissor tailed Flycatcher", "Spotted Catbird", "Summer Tanager", "Tree Swallow",
+    "Tropical Kingbird", "Vermilion Flycatcher", "Vesper Sparrow", "Warbling Vireo",
+    "Western Meadowlark", "Western Wood Pewee", "Whip poor Will", "White Pelican",
+    "White breasted Kingfisher", "White breasted Nuthatch", "White crowned Sparrow", "White throated Sparrow",
+    "Yellow Warbler", "Yellow breasted Chat", "Yellow headed Blackbird", "Yellow throated Vireo"
+]
+
+# ─── 1) CACHING MODEL LOADERS ──────────────────────────────
+@st.cache_resource
+def load_yolo_model() -> YOLO:
+    path = hf_hub_download(
+        repo_id="arpit8210/cubmodels",
+        filename="epoch45.pt",
+    )
+    return YOLO(path)
+
+@st.cache_resource
+def load_resnet_model(device: str = "cpu") -> torch.nn.Module:
+    sd_path = hf_hub_download(
+        repo_id="arpit8210/cubmodels",
+        filename="model_state_dict_best.pth",
+    )
+    model = models.resnet152(weights=None)
+    model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
+
+    raw_sd = torch.load(sd_path, map_location="cpu")
+    new_sd = {
+        (k[len("module."): ] if k.startswith("module.") else k): v
+        for k, v in raw_sd.items()
+    }
+    model.load_state_dict(new_sd)
+
+    model = torch.nn.DataParallel(model).to(device).eval()
+    return model
+
+@st.cache_resource
+def load_swin_pipeline():
+    return pipeline("image-classification",
+                    model="Emiel/cub-200-bird-classifier-swin")
+
+# ─── 2) TRANSFORM FOR RESNET ─────────────────────────────────
+resnet_transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+# ─── 3) STREAMLIT UI ────────────────────────────────────────
+st.title("🐦 Bird Species Classification")
+
+uploaded = st.file_uploader("Upload a bird image", type=["jpg", "jpeg", "png"])
+if not uploaded:
+    st.info("Please upload an image.")
+    st.stop()
+
+img = Image.open(uploaded).convert("RGB")
+st.image(img, caption="Uploaded Image", use_container_width=True)
+
+# Load models (no args here)
+yolo_model   = load_yolo_model()
+resnet_model = load_resnet_model()
+hf_pipe      = load_swin_pipeline()
+
+# Run YOLO detection
+results = yolo_model(np.array(img))
+boxes   = results[0].boxes.xyxy.cpu().numpy()
+
+annotated = img.copy()
+draw = ImageDraw.Draw(annotated)
+
+# Precompute HF result on the full image
+hf_res_full = hf_pipe(img)
+hf_label, hf_conf = hf_res_full[0]["label"], hf_res_full[0]["score"]
+
+for x1, y1, x2, y2 in boxes.astype(int):
+    crop = img.crop((x1, y1, x2, y2))
+
+    # ResNet prediction on the crop
+    inp = resnet_transform(crop).unsqueeze(0)
+    with torch.no_grad():
+        out   = resnet_model(inp)
+        probs = torch.softmax(out, dim=1)[0]
+        ridx  = torch.argmax(probs).item()
+        rlabel, rconf = class_names[ridx], probs[ridx].item()
+
+    # Choose best
+    if hf_conf > rconf:
+        label, score = hf_label, hf_conf
+    else:
+        label, score = rlabel, rconf
+
+    draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
+    draw.text((x1, max(0, y1-15)),
+              f"{label} ({score:.2f})",
+              fill="red")
+
+st.image(annotated, caption="Detections & Predictions", use_container_width=True)

requirements.txt

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+streamlit>=1.32
+torch>=2.1
+torchvision>=0.16
+ultralytics>=8.0
+transformers>=4.39
+Pillow>=9.0
+numpy>=1.24
+scikit-learn>=1.3
+huggingface-hub>=0.16.0
+matplotlib>=3.7
+tqdm>=4.66