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Makefile

@@ -4,4 +4,7 @@ build:
 	docker build -t $(APP) .
 
 run: 
-	docker run -it --rm --gpus all -p 7860:7860 $(APP)
+	docker run -it --rm --gpus all -p 7860:7860 $(APP)
+
+run-local:
+	python3 app.py

app.py

@@ -21,8 +21,8 @@ from functools import partial
 app = FastAPI()
 
 # Load the model
-model: YOLO = YOLO('model/autodistill_best.pt')  # Path to trained model
-seg_model: YOLO = YOLO('model/autodistill_best_seg.pt')  # Path to trained model
+# model: YOLO = YOLO('model/autodistill_best.pt')  # Path to trained model
+# seg_model: YOLO = YOLO('model/autodistill_best_seg.pt')  # Path to trained model
 
 # Directories
 image_dir = './pdf_images/'
@@ -43,9 +43,9 @@ HTML = """
 
 # sam = SAM()
 
-@app.get("/")
-def read_main():
-    return HTMLResponse(HTML)
+# @app.get("/")
+# def read_main():
+#     return HTMLResponse(HTML)
 
 
 # @app.get("/foo")
@@ -53,14 +53,15 @@ def read_main():
 #     return RedirectResponse("/gradio")
 
 
-def detect_solar_panel(image) -> Results:
-    # Perform inference
-    results: Results = model(image)
-    return results    
+# def detect_solar_panel(image) -> Results:
+#     # Perform inference
+#     results: Results = model(image)
+#     return results    
 
 
 def segment_solar_panel(image) -> Results:
     # Perform inference
+    seg_model: YOLO = YOLO('model/autodistill_best_seg.pt')
     results: Results = seg_model.predict(image, imgsz=(841, 595), retina_masks=True)
     return results
 
@@ -236,7 +237,7 @@ def extract_image(uploadFile: UploadFile) -> FileResponse:
     jpeg_image.save(filename)
     return FileResponse(filename)
 
-app = gr.mount_gradio_app(app, demo, path="/gradio")
+app = gr.mount_gradio_app(app, demo, path="/")
 
 if __name__ == "__main__":
     # app = gr.mount_gradio_app(app, demo, path="/gradio")