Update app.py

app.py

@@ -16,11 +16,11 @@ theme = gr.themes.Base(
 API_KEY = os.getenv("API_KEY")
 
 # BRAIN_TUMOR_API_URL = "https://api-inference.huggingface.co/models/Devarshi/Brain_Tumor_Classification"
-# BREAST_CANCER_API_URL = "https://api-inference.huggingface.co/models/MUmairAB/Breast_Cancer_Detector"
+BREAST_CANCER_API_URL = "https://api-inference.huggingface.co/models/MUmairAB/Breast_Cancer_Detector"
 # ALZHEIMER_API_URL = "https://api-inference.huggingface.co/models/AhmadHakami/alzheimer-image-classification-google-vit-base-patch16"
 # headers = {"Authorization": "Bearer "+API_KEY+"", 'Content-Type': 'application/json'}
 alzheimer_classifier = pipeline("image-classification", model="AhmadHakami/alzheimer-image-classification-google-vit-base-patch16")
-breast_cancer_classifier = pipeline("image-classification", model="MUmairAB/Breast_Cancer_Detector")
+# breast_cancer_classifier = pipeline("image-classification", model="MUmairAB/Breast_Cancer_Detector")
 brain_tumor_classifier = pipeline("image-classification", model="Devarshi/Brain_Tumor_Classification")
 
 # Create a function to Detect/Classify Alzheimer
@@ -34,11 +34,18 @@ def classify_alzheimer(image):
 
 # Create a function to Detect/Classify Breast_Cancer
 def classify_breast_cancer(image):
-    result = breast_cancer_classifier(image)
-    prediction = result[0]
-    score = prediction['score']
-    label = prediction['label']
-    return {"score": score, "label": label}
+    image_data = np.array(image, dtype=np.uint8)
+    _, buffer = cv2.imencode('.jpg', image_data)
+    binary_data = buffer.tobytes()
+
+    response = requests.post(BREAST_CANCER_API_URL, headers=headers, data=binary_data)
+    result = {}
+    print(response.json())
+    for ele in response.json():
+        label, score = ele.values()
+        result[label] = score
+
+    return result
 
 
 # Create a function to Detect/Classify Brain_Tumor