Update app.py

app.py

@@ -1,144 +1,144 @@
-# app.py
-from fastapi import FastAPI, UploadFile, Form, BackgroundTasks
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
-from pathlib import Path
-import subprocess
-import shutil
-import time
-import traceback
-import cloudinary
-import cloudinary.uploader
-from io import BytesIO
-from huggingface_hub import hf_hub_download
-
-app = FastAPI(
-    title="🚦 VRU Detection & Forecasting API",
-    description="Backend API for VRU Detection, Tracking & Forecasting pipeline (Hugging Face Spaces Edition)",
-    version="1.0.0"
-)
-
-# ==============================
-# CONFIG
-# ==============================
-# Configure Cloudinary (you can put these in .env instead)
-cloudinary.config(
-    cloud_name="YOUR_CLOUD_NAME",
-    api_key="YOUR_API_KEY",
-    api_secret="YOUR_API_SECRET"
-)
-
-SCRIPTS = [
-    ("Video Creation", "video_creation.py"),
-    ("YOLO + DeepSORT Tracking", "yolo_deepsort_tracker.py"),
-    ("Excel Generation", "excel_generation.py"),
-    ("Feature Engineering", "feature_engineering_forecasting.py"),
-    ("Trajectory Forecasting (Transformer)", "vru_forecasting_transformer.py"),
-    ("Trajectory Visualization", "animated_visualization.py")
-]
-
-OUTPUT_GIF = Path("trajectory_comparison.gif")
-
-# ==============================
-# CORS
-# ==============================
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],   # later replace with your frontend URL
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# ==============================
-# UTILS
-# ==============================
-def run_pipeline(input_path: str):
-    """Sequentially run all VRU pipeline scripts."""
-    total = len(SCRIPTS)
-    progress = []
-    for i, (label, script) in enumerate(SCRIPTS):
-        stage_info = {
-            "stage": i + 1,
-            "label": label,
-            "status": "running",
-            "progress": round((i + 1) / total, 2)
-        }
-        progress.append(stage_info)
-        try:
-            result = subprocess.run(
-                ["python", script, input_path],
-                capture_output=True, text=True
-            )
-            if result.returncode == 0:
-                stage_info["status"] = "completed"
-                stage_info["output"] = result.stdout
-            else:
-                stage_info["status"] = "failed"
-                stage_info["error"] = result.stderr
-                return {"status": "failed", "progress": progress}
-        except Exception as e:
-            stage_info["status"] = "error"
-            stage_info["error"] = str(e)
-            return {"status": "failed", "progress": progress}
-        time.sleep(0.5)
-    return {"status": "completed", "progress": progress}
-
-
-# ==============================
-# ROUTES
-# ==============================
-@app.get("/")
-def home():
-    return {"message": "🚦 VRU Detection Backend Running on Hugging Face Spaces!"}
-
-
-@app.post("/run_pipeline/")
-async def run_vru_pipeline(
-    background_tasks: BackgroundTasks,
-    dataset_name: str = Form(None),
-    file: UploadFile = None
-):
-    """
-    Run full VRU pipeline.
-    Accepts either uploaded file or Hugging Face dataset name.
-    """
-
-    try:
-        # Handle file upload
-        if file:
-            temp_path = Path("uploaded_" + file.filename)
-            with open(temp_path, "wb") as buffer:
-                shutil.copyfileobj(file.file, buffer)
-            input_path = str(temp_path)
-
-        # Handle Hugging Face dataset file
-        elif dataset_name:
-            input_path = hf_hub_download(
-                repo_id=dataset_name,
-                filename="ring_side_left/video.mp4"  # Adjust if structure differs
-            )
-
-        else:
-            return JSONResponse({"error": "Please provide a dataset_name or upload a file."}, status_code=400)
-
-        # Run pipeline
-        result = run_pipeline(input_path)
-        response = {"input_path": input_path, "result": result}
-
-        # Upload visualization if available
-        if OUTPUT_GIF.exists():
-            upload_result = cloudinary.uploader.upload(
-                str(OUTPUT_GIF),
-                resource_type="image",
-                folder="vru_results/"
-            )
-            response["visualization_url"] = upload_result["secure_url"]
-
-        return JSONResponse(response)
-
-    except Exception as e:
-        return JSONResponse(
-            {"error": str(e), "trace": traceback.format_exc()},
-            status_code=500
-        )
+# app.py
+from fastapi import FastAPI, UploadFile, Form, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from pathlib import Path
+import subprocess
+import shutil
+import time
+import traceback
+import cloudinary
+import cloudinary.uploader
+from io import BytesIO
+from huggingface_hub import hf_hub_download
+
+app = FastAPI(
+    title="🚦 VRU Detection & Forecasting API",
+    description="Backend API for VRU Detection, Tracking & Forecasting pipeline (Hugging Face Spaces Edition)",
+    version="1.0.0"
+)
+
+# ==============================
+# CONFIG
+# ==============================
+# Configure Cloudinary (you can put these in .env instead)
+cloudinary.config(
+    cloud_name="YOUR_CLOUD_NAME",
+    api_key="YOUR_API_KEY",
+    api_secret="YOUR_API_SECRET"
+)
+
+SCRIPTS = [
+    ("Video Creation", "video_creation.py"),
+    ("YOLO + DeepSORT Tracking", "yolo_deepsort_tracker.py"),
+    ("Excel Generation", "excel_generation.py"),
+    ("Feature Engineering", "feature_engineering_forecasting.py"),
+    ("Trajectory Forecasting (Transformer)", "vru_forecasting_transformer.py"),
+    ("Trajectory Visualization", "animated_visualization.py")
+]
+
+OUTPUT_GIF = Path("trajectory_comparison.gif")
+
+# ==============================
+# CORS
+# ==============================
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://vru-detection.vercel.app/"],   # later replace with your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ==============================
+# UTILS
+# ==============================
+def run_pipeline(input_path: str):
+    """Sequentially run all VRU pipeline scripts."""
+    total = len(SCRIPTS)
+    progress = []
+    for i, (label, script) in enumerate(SCRIPTS):
+        stage_info = {
+            "stage": i + 1,
+            "label": label,
+            "status": "running",
+            "progress": round((i + 1) / total, 2)
+        }
+        progress.append(stage_info)
+        try:
+            result = subprocess.run(
+                ["python", script, input_path],
+                capture_output=True, text=True
+            )
+            if result.returncode == 0:
+                stage_info["status"] = "completed"
+                stage_info["output"] = result.stdout
+            else:
+                stage_info["status"] = "failed"
+                stage_info["error"] = result.stderr
+                return {"status": "failed", "progress": progress}
+        except Exception as e:
+            stage_info["status"] = "error"
+            stage_info["error"] = str(e)
+            return {"status": "failed", "progress": progress}
+        time.sleep(0.5)
+    return {"status": "completed", "progress": progress}
+
+
+# ==============================
+# ROUTES
+# ==============================
+@app.get("/")
+def home():
+    return {"message": "🚦 VRU Detection Backend Running on Hugging Face Spaces!"}
+
+
+@app.post("/run_pipeline/")
+async def run_vru_pipeline(
+    background_tasks: BackgroundTasks,
+    dataset_name: str = Form(None),
+    file: UploadFile = None
+):
+    """
+    Run full VRU pipeline.
+    Accepts either uploaded file or Hugging Face dataset name.
+    """
+
+    try:
+        # Handle file upload
+        if file:
+            temp_path = Path("uploaded_" + file.filename)
+            with open(temp_path, "wb") as buffer:
+                shutil.copyfileobj(file.file, buffer)
+            input_path = str(temp_path)
+
+        # Handle Hugging Face dataset file
+        elif dataset_name:
+            input_path = hf_hub_download(
+                repo_id=dataset_name,
+                filename="ring_side_left/video.mp4"  # Adjust if structure differs
+            )
+
+        else:
+            return JSONResponse({"error": "Please provide a dataset_name or upload a file."}, status_code=400)
+
+        # Run pipeline
+        result = run_pipeline(input_path)
+        response = {"input_path": input_path, "result": result}
+
+        # Upload visualization if available
+        if OUTPUT_GIF.exists():
+            upload_result = cloudinary.uploader.upload(
+                str(OUTPUT_GIF),
+                resource_type="image",
+                folder="vru_results/"
+            )
+            response["visualization_url"] = upload_result["secure_url"]
+
+        return JSONResponse(response)
+
+    except Exception as e:
+        return JSONResponse(
+            {"error": str(e), "trace": traceback.format_exc()},
+            status_code=500
+        )