Gradio app.py

app.py

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+import gradio as gr
+import json
+
+from transformers import pipeline
+from pytesseract import pytesseract
+
+
+# Load the model
+nlp = pipeline(
+    "document-question-answering",
+    model="impira/layoutlm-document-qa",
+)
+
+
+# Function to perform the question answering
+def perform_question_answering(image, question):
+    answer = nlp(image, question)
+
+    # Format the answer as JSON
+    answer_json = json.dumps(answer, indent=4)
+
+    return answer_json
+
+
+# Create the Gradio interface
+inputs = [
+    gr.inputs.Image(type="pil", label="Upload Image"),
+    gr.inputs.Textbox(label="Question")
+]
+
+outputs = gr.outputs.Textbox(label="Answer")
+
+iface = gr.Interface(fn=perform_question_answering, inputs=inputs, outputs="text")
+
+
+# Launch the Gradio interface
+iface.launch()