add: app.py API calls

app.py

@@ -0,0 +1,61 @@
+# app.py
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from model import load_model, predict
+import time
+import os
+
+app = FastAPI(
+    title="ISL Recognition API",
+    description="Indian Sign Language recognition using Swin3D-S",
+    version="1.0.0"
+)
+
+# Allow all origins so your Flutter app can talk to it
+app.add_middleware(
+    CORSMiddleware,    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global variable for the model
+model = None
+
+@app.on_event("startup")
+async def startup_event():
+    global model
+    # This calls the function in your model.py to download and load the .pt file
+    model = load_model()
+    print("Model loaded and API is ready!")
+
+@app.get("/")
+def root():
+    return {"status": "ISL API is running", "message": "Send a POST request to /predict"}
+
+@app.post("/predict")
+async def predict_sign(file: UploadFile = File(...), top_k: int = 5):
+    # Validate that it's a video
+    if not file.filename.lower().endswith(('.mp4', '.mov', '.avi', '.mkv')):
+        raise HTTPException(
+            status_code=400,
+            detail="Invalid file type. Please upload a video (.mp4, .mov, etc.)"
+        )
+
+    if model is None:
+        raise HTTPException(status_code=503, detail="Model is still loading...")
+
+    start_time = time.time()
+    video_bytes = await file.read()
+    
+    # This calls the prediction logic in your model.py
+    result = predict(model, video_bytes, top_k=top_k)
+    
+    result["inference_time_ms"] = round((time.time() - start_time) * 1000, 2)
+    result["filename"] = file.filename
+    
+    return result
+
+if __name__ == "__main__":
+    # Hugging Face Spaces usually look for port 7860
+    uvicorn.run("app.py:app", host="0.0.0.0", port=7860)