Update leaderboard from GitHub main branch

app.py

@@ -297,7 +297,7 @@ with gr.Blocks(css=custom_css, title="LLM Enzyme Kinetics Extraction Benchmark")
                     label="Model Provider"
                 )
                 ocr_type_dropdown = gr.Dropdown(
-                    choices=["All", "mathpix", "kimi", "pymupdf"],
+                    choices=["All", "mathpix", "kimi", "pymupdf", "glm_ocr"],
                     value="All",
                     label="OCR Type"
                 )
@@ -424,7 +424,7 @@ with gr.Blocks(css=custom_css, title="LLM Enzyme Kinetics Extraction Benchmark")
 
             with gr.Row():
                 ocr_type_input = gr.Dropdown(
-                    choices=["mathpix", "kimi", "pymupdf"],
+                    choices=["mathpix", "kimi", "pymupdf", "glm_ocr"],
                     value="mathpix",
                     label="OCR Type *",
                     info="Which OCR version to use for evaluation"
@@ -579,7 +579,7 @@ with gr.Blocks(css=custom_css, title="LLM Enzyme Kinetics Extraction Benchmark")
 
             with gr.Row():
                 ocr_type_input = gr.Dropdown(
-                    choices=["mathpix", "kimi", "pymupdf"],
+                    choices=["mathpix", "kimi", "pymupdf", "glm_ocr"],
                     label="OCR Type *"
                 )
                 submitter_input = gr.Textbox(label="Submitter Name/Email *", placeholder="Your name or contact")

auto_eval.py

@@ -41,7 +41,7 @@ class BenchmarkEvaluator:
             api_base: API base URL
             model_name: Model name
             provider: Provider name (OpenAI, Anthropic, etc.)
-            ocr_type: OCR type (mathpix, kimi, pymupdf)
+            ocr_type: OCR type (mathpix, kimi, pymupdf, glm_ocr)
             submitter: Submitter name/email
             num_papers: Number of papers to evaluate (default: 5 for testing)
 
@@ -110,19 +110,21 @@ def main():
 
     print(f"Found {len(papers)} papers to evaluate")
 
-    # Initialize extractor
+    # Initialize extractor with optimizations enabled
     provider = os.getenv('LLM_PROVIDER', 'openai').lower()
     if provider == 'openai':
         extractor = OpenAIExtractor(
             api_key=os.getenv('OPENAI_API_KEY'),
             base_url=os.getenv('OPENAI_API_BASE'),
-            model=os.getenv('OPENAI_MODEL')
+            model=os.getenv('OPENAI_MODEL'),
+            use_stream=True  # Enable streaming for faster response
         )
     else:
         extractor = AnthropicExtractor(
             api_key=os.getenv('ANTHROPIC_API_KEY'),
             base_url=os.getenv('ANTHROPIC_API_BASE'),
-            model=os.getenv('ANTHROPIC_MODEL')
+            model=os.getenv('ANTHROPIC_MODEL'),
+            use_stream=True  # Enable streaming for faster response
         )
 
     # Run extraction