Minyst
1
Project Type: Semantic Segmentation
Operating System & Browser: Windows/ Chrome
Project ID: Recycling Segmentation
Model ID: recycling-segmentation-amso6/28
This error message occurs every single time when I run android studio code which is written by flutter.
HTTPCallErrorError(description='401 Client Error: Unauthorized.
I revoke my api key and I also refresh my render server which is model server website connected by github
I followed the instruction and copied the Hosted Image Inference code but it didn’t work
I don’t know why my gmail account is flagged.
I just wanted to tell my problem.
Admin asked me to give answer so I don’t know how to give grant to admin and I gave team member code but suddenly my account flagged.
I was so frustrated.
I didn’t realized the regulation but it wasn’t intentional.
I sent messages twice time to moderators.
There wasn’t any answer.
I couldn’t replied to admin because I’m already flagged because of my roboflow team member link.
I think admin didn’t realized that I’m flagged.
My workspace email address is different from this account but I’m afraid to tell my email address because I could be flagged again.
I’m the university student and I have to finish this project until this month I mean in summer vacation.
Please help me to find out and solve the problem.
Ford
2
Hi @Minyst!
I deeply apologize for this! To help me troubleshoot, do you mind sharing a code snippet or any further context to help me troubleshoot!
Apologies again for the inconvenience.
Minyst
3
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from inference_sdk import InferenceHTTPClient
import uvicorn
import os
import tempfile
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["GET", "POST"],
allow_headers=["*"],
)
# initialize the client
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="My own api key"
)
@app.get("/")
async def root():
return {"message": "Roboflow API Server", "status": "running"}
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
# Save uploaded image as temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=f".{file.filename.split('.')[-1]}") as temp_file:
content = await file.read()
temp_file.write(content)
temp_file_path = temp_file.name
try:
# Inference on local image
result = CLIENT.infer(
temp_file_path,
model_id="recycling-segmentation-amso6/28"
)
return result
except Exception as api_error:
error_msg = str(api_error)
if "timeout" in error_msg.lower():
return {"error": "API server timeout", "status": "failed"}
elif "connection" in error_msg.lower():
return {"error": "Network connection failed", "status": "failed"}
elif "401" in error_msg or "unauthorized" in error_msg.lower():
return {"error": "API key authentication failed", "status": "failed"}
elif "404" in error_msg:
return {"error": "Model not found", "status": "failed"}
else:
return {"error": str(api_error), "status": "failed"}
finally:
# Delete temporary file
if os.path.exists(temp_file_path):
os.unlink(temp_file_path)
except Exception as e:
return {"error": str(e), "status": "failed"}
@app.post("/predict-raw")
async def predict_raw(file: UploadFile = File(...)):
return await predict(file)
if __name__ == "__main__":
port = int(os.environ.get("PORT", 8000))
uvicorn.run("main:app", host="0.0.0.0", port=port, reload=False)
Minyst
4
I think you forgot to reply my answer.
Following code is my code snippet.
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from inference_sdk import InferenceHTTPClient
import uvicorn
import os
import tempfile
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["GET", "POST"],
allow_headers=["*"],
)
# initialize the client
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="My own api key"
)
@app.get("/")
async def root():
return {"message": "Roboflow API Server", "status": "running"}
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
# Save uploaded image as temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=f".{file.filename.split('.')[-1]}") as temp_file:
content = await file.read()
temp_file.write(content)
temp_file_path = temp_file.name
try:
# Inference on local image
result = CLIENT.infer(
temp_file_path,
model_id="recycling-segmentation-amso6/28"
)
return result
except Exception as api_error:
error_msg = str(api_error)
if "timeout" in error_msg.lower():
return {"error": "API server timeout", "status": "failed"}
elif "connection" in error_msg.lower():
return {"error": "Network connection failed", "status": "failed"}
elif "401" in error_msg or "unauthorized" in error_msg.lower():
return {"error": "API key authentication failed", "status": "failed"}
elif "404" in error_msg:
return {"error": "Model not found", "status": "failed"}
else:
return {"error": str(api_error), "status": "failed"}
finally:
# Delete temporary file
if os.path.exists(temp_file_path):
os.unlink(temp_file_path)
except Exception as e:
return {"error": str(e), "status": "failed"}
@app.post("/predict-raw")
async def predict_raw(file: UploadFile = File(...)):
return await predict(file)
if __name__ == "__main__":
port = int(os.environ.get("PORT", 8000))
uvicorn.run("main:app", host="0.0.0.0", port=port, reload=False)