Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import requests
+from PIL import Image
+import torch, os, re
+
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/multi_col_40777.png', 'chart_example_1.png')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/OECD_SECONDARY_GRADUATION_RATE_ESP_ITA_MEX_000019.png', 'chart_example_2.png')
+
+
+model_name = "ahmed-masry/unichart-chartqa-960"
+model = VisionEncoderDecoderModel.from_pretrained(model_name, use_auth_token=os.environ['temp_access_token'])
+processor = DonutProcessor.from_pretrained(model_name, use_auth_token=os.environ['temp_access_token'])
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+
+def predict(image, input_prompt):
+    input_prompt = "<chartqa> " + input_prompt + " <s_answer>"
+    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.max_position_embeddings,
+        early_stopping=True,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        num_beams=4,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+    sequence = processor.batch_decode(outputs.sequences)[0]
+    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    sequence = sequence.split("<s_answer>")[1].strip()
+    return sequence
+
+   
+image = gr.inputs.Image(type="pil", label="Chart Image")
+input_prompt = gr.inputs.Textbox(label="Question")
+model_output = gr.outputs.Textbox(label="Model Output")
+examples = [["chart_example_1.png", "What is the lowest value in blue bar?"],
+            ["chart_example_2.png", "Which country has highest secondary graduation rate in 2018?"]]
+
+title = "Interactive Gradio Demo for UniChart-ChartQA model"
+interface = gr.Interface(fn=predict, 
+                         inputs=[image, input_prompt], 
+                         outputs=model_output, 
+                         examples=examples, 
+                         title=title,
+                         theme='gradio/soft',
+                         enable_queue=True)
+
+interface.launch()