Update app.py

app.py

@@ -13,22 +13,40 @@ if not os.path.exists(UPLOAD_FOLDER):
     os.makedirs(UPLOAD_FOLDER)
 
 from inference_sdk import InferenceHTTPClient
-def predict_disease(filepath):    
+import base64
+
+def encode_image_to_base64(filepath):
+    with open(filepath, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
+    return encoded_string
+
+def predict_pest(filepath):
     CLIENT = InferenceHTTPClient(
-        api_url="https://classify.roboflow.com",
+        api_url="https://detect.roboflow.com",
         api_key="oF1aC4b1FBCDtK8CoKx7"
     )
     
-    result = CLIENT.infer(filepath, model_id="plant-disease-detection-iefbi/1")
-    return result['predicted_classes'][0]
+    try:
+        encoded_image = encode_image_to_base64(filepath)
+        result = CLIENT.infer(encoded_image, model_id="pest-detection-ueoco/1")
+        return result['predicted_classes'][0]
+    except Exception as e:
+        print(f"API call error: {e}")
+        return "Error during pest detection."
 
-def predict_pest(filepath):
+def predict_disease(filepath):
     CLIENT = InferenceHTTPClient(
-    api_url="https://detect.roboflow.com",
-    api_key="oF1aC4b1FBCDtK8CoKx7")
+        api_url="https://classify.roboflow.com",
+        api_key="oF1aC4b1FBCDtK8CoKx7"
+    )
     
-    result = CLIENT.infer(filepath, model_id="pest-detection-ueoco/1")
-    return result['predicted_classes'][0]
+    try:
+        encoded_image = encode_image_to_base64(filepath)
+        result = CLIENT.infer(encoded_image, model_id="plant-disease-detection-iefbi/1")
+        return result['predicted_classes'][0]
+    except Exception as e:
+        print(f"API call error: {e}")
+        return "Error during disease detection."
 
 # Initialize the Flask app
 account_sid = os.environ.get('TWILIO_ACCOUNT_SID')