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Dockerfile

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+FROM python:3.10-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# --- SYSTEM DEPENDENCIES (CRITICAL FOR OPENCV / YOLO) ---
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    gcc \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# --- PYTHON DEPENDENCIES ---
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+# --- APP CODE ---
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,8 +1,8 @@
 ---
-title: DamageLens
-emoji: 🔥
-colorFrom: blue
-colorTo: blue
+title: DamageLensAI
+emoji: 😻
+colorFrom: red
+colorTo: green
 sdk: docker
 pinned: false
 ---

app.py

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+import os
+import uuid
+import shutil
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.staticfiles import StaticFiles
+from PIL import Image
+from fastapi.middleware.cors import CORSMiddleware
+from scripts.gradcam import get_resnet_gradcam, get_deit_gradcam
+from scripts.yolo import get_yolo_damage_boxes
+from scripts.prediction_helper import ResnetCarDamagePredictor, DeitCarDamagePredictor, FusionCarDamagePredictor
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], 
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+UPLOAD_DIR = "static/uploads"
+RESULT_DIR = "static/results"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+os.makedirs(RESULT_DIR, exist_ok=True)
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+class_map = {
+    0: "Front Breakage",
+    1: "Front Crushed",
+    2: "Front Normal",
+    3: "Rear Breakage",
+    4: "Rear Crushed",
+    5: "Rear Normal"
+}
+
+resnet_checkpoint = "checkpoints/best_resnet_model.pt"
+deit_checkpoint = "checkpoints/best_deit_model.pt"
+
+
+Resnet_Model = ResnetCarDamagePredictor(resnet_checkpoint, class_map)
+Deit_Model = DeitCarDamagePredictor(deit_checkpoint, class_map)
+Fusion_Model = FusionCarDamagePredictor(resnet_predictor=Resnet_Model, deit_predictor=Deit_Model, resnet_weight=0.5, deit_weight=0.5)
+
+resnet_predictor = Resnet_Model
+deit_predictor = Deit_Model
+
+# ====================== API Endpoint ======================
+
+@app.get("/")
+def api_status():
+    return {"status": "API is running"}
+
+# ============================= Grad-CAM Generation Endpoint =============================
+
+@app.post("/predict")
+async def predict_and_generate_cams(file: UploadFile = File(...)):
+    unique_id = str(uuid.uuid4())
+    input_filename = f"{unique_id}_input.jpg"
+    resnet_out_name = f"{unique_id}_resnet.jpg"
+    deit_out_name = f"{unique_id}_deit.jpg"
+
+    input_path = os.path.join(UPLOAD_DIR, input_filename)
+    resnet_path = os.path.join(RESULT_DIR, resnet_out_name)
+    deit_path = os.path.join(RESULT_DIR, deit_out_name)
+
+    # Save uploaded file
+    with open(input_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+
+    # Generate Grad-CAMs
+    get_resnet_gradcam(input_path, resnet_predictor, resnet_path)
+    get_deit_gradcam(input_path, deit_predictor, deit_path)
+
+    # Return the URLs
+    return {
+        "status": "success",
+        "original_image": f"/static/uploads/{input_filename}",
+        "resnet_viz": f"/static/results/{resnet_out_name}",
+        "deit_viz": f"/static/results/{deit_out_name}"
+    }
+
+# ============================= Prediction-Only Endpoints =============================
+# ============================= Resnet Prediction =====================================
+
+@app.post("/predict/resnet")
+async def resnet_prediction(image : UploadFile = File(...)):
+    try:
+        image = Image.open(image.file)
+    except Exception:
+        raise HTTPException(status_code=400, detail="Invalid image file")
+    result = Resnet_Model.resnet_predict(image_input=image)
+    return result
+
+# ============================= Deit Prediction =====================================  
+@app.post("/predict/deit")
+async def deit_prediction(image : UploadFile = File(...)):
+    try:
+        image = Image.open(image.file)
+    except Exception:
+        raise HTTPException(status_code=400, detail="Invalid image file")
+    result = Deit_Model.deit_predict(image_input=image)
+    return result
+
+# ============================= Fusion Prediction ===================================== 
+@app.post("/predict/fusion")
+async def fusion_prediction(image : UploadFile = File(...)):
+    try:
+        image = Image.open(image.file)
+    except Exception:
+        raise HTTPException(status_code=400, detail="Invalid image file")
+    result = Fusion_Model.fuse_predict(image_input=image)
+    return result
+
+# ============================= YOLO Damage Box Endpoint =============================
+@app.post("/predict/yolo")
+async def yolo_detection(file: UploadFile = File(...)):
+    unique_id = str(uuid.uuid4())
+
+    input_filename = f"{unique_id}_input.jpg"
+    yolo_out_name = f"{unique_id}_yolo.jpg"
+
+    input_path = os.path.join(UPLOAD_DIR, input_filename)
+    yolo_path = os.path.join(RESULT_DIR, yolo_out_name)
+
+    with open(input_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+
+    result = get_yolo_damage_boxes(input_path, yolo_path)
+
+    return {
+        "status": "success",
+        "original_image": f"/static/uploads/{input_filename}",
+        "yolo_image": f"/static/results/{yolo_out_name}",
+        "detections": result["detections"],
+        "total_detections": result["total_detections"],
+        "message": result["message"]
+    }

requirements.txt

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+torch
+torchvision
+transformers
+fastapi
+uvicorn
+dotenv
+matplotlib
+opencv-python
+python-multipart
+ultralytics
+plotly
+pandas