Update app.py

app.py

@@ -186,13 +186,13 @@ def process_document(llm, num_shot, image, question):
     table = processor_deplot.decode(predictions[0], skip_special_tokens=True).replace("<0x0A>", "\n")
 
     # send prompt+table to LLM
-    res = evaluate(table, num_shot, question, llm=llm)
+    res = evaluate(table, question, llm=llm, num_shot=num_shot)
     if llm == "alpaca-lora":
         return [table, res.split("A:")[-1]]
     else:
         return [table, res]
  
-description = "Demo for DePlot+LLM for QA and summarisation. [DePlot](https://arxiv.org/abs/2212.10505) is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLM is [alpaca-lora](https://huggingface.co/spaces/tloen/alpaca-lora). To use it, simply upload your image and type a question or instruction and click 'submit', or click one of the examples to load them. Read more at the links below."
+description = "Demo for DePlot+LLM for QA and summarisation. [DePlot](https://arxiv.org/abs/2212.10505) is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLMs are [alpaca-lora](https://huggingface.co/spaces/tloen/alpaca-lora) and [flan-ul2](https://huggingface.co/google/flan-ul2). To use it, simply upload your image and type a question or instruction and click 'submit', or click one of the examples to load them. Read more at the links below."
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2212.10505' target='_blank'>DePlot: One-shot visual language reasoning by plot-to-table translation</a></p>"
 
 demo = gr.Interface(