Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForSeq2SeqLM
+import requests
+from PIL import Image
+import torch, os, re, json
+import spaces
+
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/74801584018932.png', 'chart_example_1.png')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/multi_col_1229.png', 'chart_example_2.png')
+
+
+
+model = AutoModelForSeq2SeqLM.from_pretrained("ahmed-masry/ChartInstruct-FlanT5-XL", torch_dtype=torch.float16, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("ahmed-masry/ChartInstruct-FlanT5-XL")
+
+
+@spaces.GPU
+def predict(image, input_text):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+
+    input_prompt = f"<image>\n Question: {input_text} Answer: "
+    image = image.convert("RGB")
+
+    inputs = processor(text=input_prompt, images=image, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    # change type if pixel_values in inputs to fp16. 
+    inputs['pixel_values'] = inputs['pixel_values'].to(torch.float16)
+    
+    # Generate
+    generate_ids = model.generate(**inputs, num_beams=4, max_new_tokens=512)
+    output_text = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+    return output_text
+
+   
+image = gr.components.Image(type="pil", label="Chart Image")
+input_prompt = gr.components.Textbox(label="Input Prompt")
+model_output = gr.components.Textbox(label="Model Output")
+examples = [["chart_example_1.png", "Describe the trend of the mortality rates for the Neonatal"],
+            ["chart_example_2.png", "What is the share of respondants who prefer Facebook Messenger in the 30-59 age group?"]]
+
+title = "Interactive Gradio Demo for ChartInstruct-FlanT5-XL model"
+interface = gr.Interface(fn=predict, 
+                         inputs=[image, input_prompt], 
+                         outputs=model_output, 
+                         examples=examples, 
+                         title=title,
+                         theme='gradio/soft')
+
+interface.launch()