feat(api): add retry method to zeroshot calling

backend/utils.py

@@ -5,6 +5,7 @@
 import os
 import requests
 # from openai import OpenAI
+import time
 from groq import Groq
 import base64
 
@@ -33,31 +34,40 @@ async def predict(new_image_path):
         return f"Error processing image: {str(e)}", 0
 
 async def zeroshot(new_image_path):
-    try:
-        API_URL = "https://api-inference.huggingface.co/models/openai/clip-vit-large-patch14-336"
-        headers = {"Authorization": f"Bearer {HF_API}"}
+    retry_count = 0
+    max_retries = 3
+
+    while retry_count < max_retries:
+        try:
+            API_URL = "https://api-inference.huggingface.co/models/openai/clip-vit-large-patch14-336"
+            headers = {"Authorization": f"Bearer {HF_API}"}
 
-        def query(data):
-            with open(data["image_path"], "rb") as f:
-                img = f.read()
-            payload={
-                "parameters": data["parameters"],
-                "inputs": base64.b64encode(img).decode("utf-8")
-            }
-            response = requests.post(API_URL, headers=headers, json=payload)
-            return response.json()
+            def query(data):
+                with open(data["image_path"], "rb") as f:
+                    img = f.read()
+                payload={
+                    "parameters": data["parameters"],
+                    "inputs": base64.b64encode(img).decode("utf-8")
+                }
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json()
 
-        output = query({
-            "image_path": f"{new_image_path}",
-            "parameters": {"candidate_labels": output_class},
-        })
-        max_component = max(output, key=lambda x: x['score'])
-        predicted_value, predicted_accuracy = max_component['label'], max_component['score']
-        return predicted_value, predicted_accuracy
+            output = query({
+                "image_path": f"{new_image_path}",
+                "parameters": {"candidate_labels": output_class},
+            })
+            max_component = max(output, key=lambda x: x['score'])
+            predicted_value, predicted_accuracy = max_component['label'], max_component['score']
+            return predicted_value, predicted_accuracy
 
-    except Exception as e:
-        print(f"Error processing image: {str(e)}")
-        return "unknown", 0
+        except Exception as e:
+            print(f"Error processing image: {str(e)}")
+            retry_count += 1
+            if retry_count < max_retries:
+                print("Retrying...")
+                time.sleep(1)
+
+    return "unknown", 0
 
 
 def input_trash(input):