using API

gender_class.py

@@ -0,0 +1,14 @@
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/rizvandwiki/gender-classification-2"
+headers = {"Authorization": "Bearer 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"}
+
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
+
+output = query("test.jpg")
+
+print(output)

main.py

@@ -12,8 +12,12 @@ from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 import subprocess
 from hairstyle_recommendation import HairstyleRecommendation
+import requests
 
+# A FUCKING PI 
 app = FastAPI()
+API_URL = "https://api-inference.huggingface.co/models/rizvandwiki/gender-classification-2"
+headers = {"Authorization": "Bearer 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"}
 public_url = "https://lambang0902-test-space.hf.space"
 app.mount("/static", StaticFiles(directory="static"), name="static")
 
@@ -58,6 +62,12 @@ from train_pred import TrainPred
 data_processor = DataProcessing()
 data_train_pred = TrainPred()
 
+def get_gender(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
+
 import random
 def preprocessing(filepath):
     folder_path = './static/temporary'
@@ -107,9 +117,9 @@ async def root():
 #                   API UNTUK MELAKUKAN PROSES PREDIKSI
 # -------------------------------------------------------------------------
 # Use a pipeline as a high-level helper
-from transformers import pipeline
+# from transformers import pipeline
 
-pipe = pipeline("image-classification", model="rizvandwiki/gender-classification-2")
+# pipe = pipeline("image-classification", model="rizvandwiki/gender-classification-2")
 
 @app.post('/upload/file',tags=["Predicting"])
 async def upload_file(picture: UploadFile):
@@ -147,7 +157,7 @@ def get_images():
     bentuk, persentase = data_train_pred.prediction(selected_model)
 
     # Gender classification
-    gender_classify = pipe('./static/result_upload0.jpg')
+    gender_classify = get_gender('./static/result_upload0.jpg')
     output_gender = max(gender_classify, key=lambda x: x['score'])['label']