Update app.py

app.py

@@ -10,7 +10,7 @@ torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQ
 torch.hub.download_url_to_file('https://sharkcoder.com/files/article/matplotlib-bar-plot.png', 'chart_example_4.png')
 
 
-model_name = "google/matcha-chart2text-statista"
+model_name = "google/matcha-chartqa"
 model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
 processor = Pix2StructProcessor.from_pretrained(model_name)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -27,10 +27,11 @@ def summarize_chart(image):
    
 image = gr.inputs.Image(type="pil", label="Chart")
 answer = gr.outputs.Textbox(label="Chart Summary")
-examples = [["chart_example.png"], ["chart_example_2.png"], ["chart_example_3.png"], ["chart_example_4.png"]]
+examples = [["chart_example.png", "Which country has the second highest death rate?"], ]
+            #["chart_example_2.png"], ["chart_example_3.png"], ["chart_example_4.png"]]
 
-title = "Interactive demo: chart summarization"
-description = "Gradio Demo for matcha model, fine-tuned on chart2text statista, a model that can summarize charts. To use it, simply upload your image and click 'submit', or click one of the examples to load them."
+title = "Interactive demo: chart QA"
+description = "Gradio Demo for matcha model, fine-tuned on the ChartQA dataset. To use it, simply upload your image and click 'submit', or click one of the examples to load them."
 
 interface = gr.Interface(fn=summarize_chart, 
                          inputs=[image],