bug fixed

app.py

@@ -1,21 +1,58 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
 import torch
+import pandas as pd
+import uvicorn
 import json
-import gradio as gr
+from PIL import Image, UnidentifiedImageError
+import io
+import os
+from datetime import datetime
 
 # Load YOLOv5s pretrained on COCO
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
 
-def detect(image):
+# Folder to store annotated images
+SAVE_DIR = "detections"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+app = FastAPI(title="COCO Object Detection API")
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Read uploaded file into bytes
+        image_bytes = await file.read()
+
+        # Open with Pillow safely
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    except UnidentifiedImageError:
+        return JSONResponse(
+            content={"error": "Unrecognized or invalid image file format."},
+            status_code=400
+        )
+
+    # Run inference
     results = model(image)
-    df = results.pandas().xyxy[0]  # xmin, ymin, xmax, ymax, confidence, class, name
-    return json.loads(df.to_json(orient="records"))
-
-iface = gr.Interface(
-    fn=detect,
-    inputs=gr.Image(type="pil"),
-    outputs="json",
-    title="COCO Object Detection (YOLOv5)",
-    description="Upload an image to detect objects (COCO classes). Output is JSON."
-)
-
-iface.launch()
+
+    # Save annotated image locally
+    plotted_image = results.render()[0]  # numpy array with boxes
+    pil_image = Image.fromarray(plotted_image)
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    save_path = os.path.join(SAVE_DIR, f"detection_{timestamp}.jpg")
+    pil_image.save(save_path, format="JPEG")
+    print (f"Saved annotated image to {save_path}")
+
+    # Convert results to JSON
+    df: pd.DataFrame = results.pandas().xyxy[0]
+    json_data = json.loads(df.to_json(orient="records"))
+
+    return JSONResponse(content=json_data)
+
+@app.get("/")
+def root():
+    return {"message": "Send POST /predict with an image file to get detections."}
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8080)

requirements.txt

@@ -2,3 +2,7 @@ torch
 torchvision
 pillow
 gradio
+ultralytics
+seaborn
+fastapi
+uvicorn