raise exception if cannot parse answer, do not return an arbitrary an…

…swer

textgrad/tasks/multimodal/mathvista.py

@@ -11,10 +11,6 @@
 except ImportError:
     raise ImportError("Please install the Levenshtein package using 'pip install python-Levenshtein' to use mathvista.")
 
-from textgrad.engine.openai import ChatOpenAI
-
-local_llm_engine = ChatOpenAI(model_string="gpt-3.5-turbo", is_multimodal=False)
-print("Local OpenAI engine initialized.\n")
 
 def compress_image(decoded_image, max_size_bytes=3.6*1024*1024):
     # Convert image to RGB if it's in a mode that JPEG does not support
@@ -31,7 +27,6 @@ def compress_image(decoded_image, max_size_bytes=3.6*1024*1024):
 
     width, height = decoded_image.size
     while size > max_size_bytes:
-        print(f"Compressing image to {width}x{height}...")
         width = int(width * 0.9)
         height = int(height * 0.9)
         resized_image = decoded_image.resize((width, height), Image.LANCZOS)
@@ -131,26 +126,24 @@ def extract_answer(response, problem, quick_extract=False):
 
     # quick extraction
     if quick_extract:
-        print("Quickly extracting answer...")
-        # The answer is "text". -> "text"
         try:
             result = re.search(r'The answer is "(.*)"\.', response)
             if result:
                 extraction = result.group(1)
                 return extraction
-        except:
-            pass
+        except Exception as e:
+            raise Exception(f"Error in extracting answer for {pid}: {e}. Remove this line responsibly.")
 
     # general extraction
     try:
+        from textgrad.engine.openai import ChatOpenAI
+        local_llm_engine = ChatOpenAI(model_string="gpt-3.5-turbo", is_multimodal=False)
+
         full_prompt = create_test_prompt(demo_prompt, query, response)
         extraction = local_llm_engine(full_prompt)
         return extraction
     except Exception as e:
-        print(e)
-        print(f"Error in extracting answer for {pid}")
-
-    return ""
+        raise Exception(f"Error in extracting answer for {pid}: {e}")
 
 
 def get_most_similar(prediction, choices):