Update app.py

app.py

@@ -14,56 +14,39 @@ 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"
-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'}
-
+# 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"
+# 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")
+brain_tumor_classifier = pipeline("image-classification", model="Devarshi/Brain_Tumor_Classification")
 
 # Create a function to Detect/Classify Alzheimer
 def classify_alzheimer(image):
-    image_data = np.array(image, dtype=np.uint8)
-    _, buffer = cv2.imencode('.jpg', image_data)
-    binary_data = buffer.tobytes()
-
-    response = requests.post(ALZHEIMER_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
+    result = alzheimer_classifier(image)
+    prediction = result[0]
+    score = prediction['score']
+    label = prediction['label']
+    return {"score": score, "label": label}
 
 
 # Create a function to Detect/Classify Breast_Cancer
 def classify_breast_cancer(image):
-    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 = {}
-    for ele in response:
-        label, score = ele.values()
-        result[label] = score
-
-    return result
+    result = breast_cancer_classifier(image)
+    prediction = result[0]
+    score = prediction['score']
+    label = prediction['label']
+    return {"score": score, "label": label}
 
 
 # Create a function to Detect/Classify Brain_Tumor
 def classify_brain_tumor(image):
-    image_data = np.array(image, dtype=np.uint8)
-    _, buffer = cv2.imencode('.jpg', image_data)
-    binary_data = buffer.tobytes()
-
-    response = requests.post(BRAIN_TUMOR_API_URL, headers=headers, data=binary_data)
-    result = {}
-    for ele in response:
-        label, score = ele.values()
-        result[label] = score
-
-    return result
+    result = brain_tumor_classifier(image)
+    prediction = result[0]
+    score = prediction['score']
+    label = prediction['label']
+    return {"score": score, "label": label}
 
 
 # Create the Gradio interface
@@ -85,8 +68,8 @@ with gr.Blocks(theme=theme) as Alzheimer:
 
         def respond(message, history):
             bot_message = g4f.ChatCompletion.create(
-                model="gpt-4-32k-0613",
-                provider=g4f.Provider.GeekGpt,
+                model="gpt-3.5-turbo",
+                provider=g4f.Provider.Vercel,
                 messages=[{"role": "user",
                            "content": "Your role is Alzheimer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Alzheimer or not. If it is not related to Alzheimer then simply avoid the query by saying this is not my expertise, whereas if related to Alzheimer reply it as usual. Here's the user Query:" + message}],
             )
@@ -120,8 +103,8 @@ with gr.Blocks(theme=theme) as BreastCancer:
 
         def respond(message, history):
             bot_message = g4f.ChatCompletion.create(
-                model="gpt-4-32k-0613",
-                provider=g4f.Provider.GeekGpt,
+                model="gpt-3.5-turbo",
+                provider=g4f.Provider.Vercel,
                 messages=[{"role": "user",
                            "content": "Your role is Breast_Cancer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Breast_Cancer or not. If it is not related to Breast_Cancer then simply avoid the query by saying this is not my expertise, whereas if related to Breast_Cancer reply it as usual. Here's the user Query:" + message}],
             )
@@ -154,8 +137,8 @@ with gr.Blocks(theme=theme) as BrainTumor:
 
         def respond(message, history):
             bot_message = g4f.ChatCompletion.create(
-                model="gpt-4-32k-0613",
-                provider=g4f.Provider.GeekGpt,
+                model="gpt-3.5-turbo",
+                provider=g4f.Provider.Vercel,
                 messages=[{"role": "user",
                            "content": "Your role is Brain Tumor Disease Expert. Now I will provide you with the user query. First check if the user query is related to Brain Tumor or not. If it is not related to Brain Tumor then simply avoid the query by saying this is not my expertise, whereas if related to Brain Tumor reply it as usual. Here's the user Query:" + message}],
             )