diff --git a/tools/prediction_request_sme.py b/tools/prediction_request_sme.py
index 066e9ce0..039559bc 100644
--- a/tools/prediction_request_sme.py
+++ b/tools/prediction_request_sme.py
@@ -28,7 +28,6 @@
 from bs4 import BeautifulSoup
 from googleapiclient.discovery import build
 
-
 NUM_URLS_EXTRACT = 5
 DEFAULT_OPENAI_SETTINGS = {
     "max_tokens": 500,
@@ -111,7 +110,6 @@
 * Output only the JSON object. Do not include any other contents in your response.
 """
 
-
 SME_GENERATION_MARKET_PROMPT = """
 task question: "{question}"
 """
@@ -150,7 +148,6 @@
 """
 
 
-
 def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]:
     service = build("customsearch", "v1", developerKey=api_key)
     search = (
@@ -170,10 +167,10 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List
     results = []
     for query in queries:
         for url in search_google(
-            query=query,
-            api_key=api_key,
-            engine=engine,
-            num=3,  # Number of returned results
+                query=query,
+                api_key=api_key,
+                engine=engine,
+                num=3,  # Number of returned results
         ):
             results.append(url)
     unique_results = list(set(results))
@@ -181,8 +178,8 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List
 
 
 def extract_text(
-    html: str,
-    num_words: int = 300,  # TODO: summerise using GPT instead of limit
+        html: str,
+        num_words: int = 300,  # TODO: summerise using GPT instead of limit
 ) -> str:
     """Extract text from a single HTML document"""
     soup = BeautifulSoup(html, "html.parser")
@@ -196,15 +193,16 @@ def extract_text(
 
 
 def process_in_batches(
-    urls: List[str], window: int = 5, timeout: int = 10
+        urls: List[str], window: int = 5, timeout: int = 10
 ) -> Generator[None, None, List[Tuple[Future, str]]]:
     """Iter URLs in batches."""
     with ThreadPoolExecutor() as executor:
         for i in range(0, len(urls), window):
-            batch = urls[i : i + window]
+            batch = urls[i: i + window]
             futures = [(executor.submit(requests.get, url, timeout=timeout), url) for url in batch]
             yield futures
 
+
 def extract_texts(urls: List[str], num_words: int = 300) -> List[str]:
     """Extract texts from URLs"""
     max_allowed = 5
@@ -225,19 +223,19 @@ def extract_texts(urls: List[str], num_words: int = 300) -> List[str]:
             except requests.exceptions.ReadTimeout:
                 print(f"Request timed out: {url}.")
             except Exception as e:
-                    print(f"An error occurred: {e}")
+                print(f"An error occurred: {e}")
         if stop:
             break
     return extracted_texts
 
 
 def fetch_additional_information(
-    prompt: str,
-    engine: str,
-    temperature: float,
-    max_tokens: int,
-    google_api_key: str,
-    google_engine: str,
+        prompt: str,
+        engine: str,
+        temperature: float,
+        max_tokens: int,
+        google_api_key: str,
+        google_engine: str,
 ) -> str:
     """Fetch additional information."""
     url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt)
@@ -288,8 +286,7 @@ def get_sme_role(engine, temperature, max_tokens, prompt) -> Tuple[str, str]:
         stop=None,
     )
     generated_sme_roles = response.choices[0].message.content
-    # check whether the generated_sme_roles is valid json
-    sme = json.loads(generated_sme_roles)
+    sme = json.loads(generated_sme_roles)[0]
     return sme["sme"], sme["sme_introduction"]
 
 
@@ -353,3 +350,14 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]:
     return response.choices[0].message.content, None
 
 
+prompt = {
+    'prompt': '"Will the Disney Royal collections be available for purchase after the World Princess Week by 29 August 2023?" and the `yes` option represented by `Yes` and the `no` option represented by `No`, what are the respective probabilities of `p_yes` and `p_no` occurring?',
+    'tool': 'prediction-online-sme', 'nonce': '3a32f8ad-efd1-43b9-adc7-967065321a10',
+    "api_keys": {
+        "openai": "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",
+        "google_api_key": "AIzaSyDgX4gLJh5BUzBGIzUDpP_moqMLP6lAoEc",
+        "google_engine_id": "23252845a8b224090",
+    },
+}
+
+print(run(**prompt))