--

app.py

@@ -1,129 +1,71 @@
-import gradio as gr
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
 from transformers import DetrImageProcessor, DetrForObjectDetection
 from PIL import Image, ImageDraw
+import io
 import torch
 
-# Load the DETR model and processor
-model_name = "hilmantm/detr-traffic-accident-detection"
-processor = DetrImageProcessor.from_pretrained(model_name)
-model = DetrForObjectDetection.from_pretrained(model_name)
+# Initialize FastAPI app
+app = FastAPI()
 
-def detect_accident(image):
-    """Process an image and detect traffic accidents using the DETR model."""
-    # Preprocess the input image
-    inputs = processor(images=image, return_tensors="pt", size={"longest_edge": 800})
-    
-    # Get model predictions
-    outputs = model(**inputs)
-    
-    # Post-process predictions to extract bounding boxes and labels
-    target_sizes = torch.tensor([image.size[::-1]])  # Image size in (height, width)
-    results = processor.post_process_object_detection(
-        outputs, target_sizes=target_sizes, threshold=0.9
-    )[0]
-    
-    # Draw bounding boxes and labels on the image
-    draw = ImageDraw.Draw(image)
-    for box, score, label in zip(results["boxes"], results["scores"], results["labels"]):
-        box = [int(b) for b in box]
-        label_text = f"{model.config.id2label[label]}: {score:.2f}"
-        draw.rectangle(box, outline="red", width=3)
-        draw.text((box[0], box[1]), label_text, fill="red")
-    
-    return image
-
-# Define the Gradio interface
-iface = gr.Interface(
-    fn=detect_accident,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Traffic Accident Detection",
-    description="Upload an image to detect traffic accidents using the DETR model."
+# Add CORS middleware to allow communication with external clients
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Change this to the specific domain in production
+    allow_methods=["*"],
+    allow_headers=["*"],
 )
 
-# Launch the app
-iface.launch()
-
-
-
+# Load the model and processor
+model = DetrForObjectDetection.from_pretrained("hilmantm/detr-traffic-accident-detection")
+processor = DetrImageProcessor.from_pretrained("hilmantm/detr-traffic-accident-detection")
 
+def detect_accident(image):
+    """Runs accident detection on the input image."""
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
 
+    # Post-process results
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
 
-
-
-
-
-
-
-
-# from fastapi import FastAPI, File, UploadFile
-# from fastapi.responses import JSONResponse
-# from fastapi.middleware.cors import CORSMiddleware
-# from transformers import DetrImageProcessor, DetrForObjectDetection
-# from PIL import Image, ImageDraw
-# import io
-# import torch
-
-# # Initialize FastAPI app
-# app = FastAPI()
-
-# # Add CORS middleware to allow communication with external clients
-# app.add_middleware(
-#     CORSMiddleware,
-#     allow_origins=["*"],  # Change this to the specific domain in production
-#     allow_methods=["*"],
-#     allow_headers=["*"],
-# )
-
-# # Load the model and processor
-# model = DetrForObjectDetection.from_pretrained("hilmantm/detr-traffic-accident-detection")
-# processor = DetrImageProcessor.from_pretrained("hilmantm/detr-traffic-accident-detection")
-
-# def detect_accident(image):
-#     """Runs accident detection on the input image."""
-#     inputs = processor(images=image, return_tensors="pt")
-#     outputs = model(**inputs)
-
-#     # Post-process results
-#     target_sizes = torch.tensor([image.size[::-1]])
-#     results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
-
-#     # Draw bounding boxes and labels
-#     draw = ImageDraw.Draw(image)
-#     for box, label, score in zip(results["boxes"], results["labels"], results["scores"]):
-#         x_min, y_min, x_max, y_max = box
-#         draw.rectangle((x_min, y_min, x_max, y_max), outline="red", width=3)
-#         draw.text((x_min, y_min), f"{label}: {score:.2f}", fill="red")
+    # Draw bounding boxes and labels
+    draw = ImageDraw.Draw(image)
+    for box, label, score in zip(results["boxes"], results["labels"], results["scores"]):
+        x_min, y_min, x_max, y_max = box
+        draw.rectangle((x_min, y_min, x_max, y_max), outline="red", width=3)
+        draw.text((x_min, y_min), f"{label}: {score:.2f}", fill="red")
     
-#     return image
+    return image
 
-# @app.post("/detect_accident")
-# async def process_frame(file: UploadFile = File(...)):
-#     """API endpoint to process an uploaded frame."""
-#     try:
-#         # Read and preprocess image
-#         image = Image.open(io.BytesIO(await file.read()))
-#         image = image.resize((256, int(image.height * 256 / image.width)))  # Resize while maintaining aspect ratio
-
-#         # Detect accidents
-#         processed_image = detect_accident(image)
-
-#         # Save the processed image into bytes to send back
-#         img_byte_arr = io.BytesIO()
-#         processed_image.save(img_byte_arr, format="JPEG")
-#         img_byte_arr.seek(0)
-
-#         return JSONResponse(
-#             content={"status": "success", "message": "Frame processed successfully"},
-#             media_type="image/jpeg"
-#         )
-#     except Exception as e:
-#         return JSONResponse(content={"status": "error", "message": str(e)}, status_code=500)
-
-# # Run the app
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
+@app.post("/detect_accident")
+async def process_frame(file: UploadFile = File(...)):
+    """API endpoint to process an uploaded frame."""
+    try:
+        # Read and preprocess image
+        image = Image.open(io.BytesIO(await file.read()))
+        image = image.resize((256, int(image.height * 256 / image.width)))  # Resize while maintaining aspect ratio
+
+        # Detect accidents
+        processed_image = detect_accident(image)
+
+        # Save the processed image into bytes to send back
+        img_byte_arr = io.BytesIO()
+        processed_image.save(img_byte_arr, format="JPEG")
+        img_byte_arr.seek(0)
+
+        return JSONResponse(
+            content={"status": "success", "message": "Frame processed successfully"},
+            media_type="image/jpeg"
+        )
+    except Exception as e:
+        return JSONResponse(content={"status": "error", "message": str(e)}, status_code=500)
+
+# Run the app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)