Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+
+from transformers import pipeline
+
+
+# Chatbot model 
+model = pipeline("document-question-answering", model="importa/layoutlm-document-qa")
+
+
+def parse_ticket_image(image, question):
+    """Basically just runs through these questions for the document"""
+    # Define questions you want to ask the model
+    
+    questions = [
+        {"question": "What is the ticket number?", "context": image},
+        {"question": "What is the date?", "context": image},
+        {"question": "What is the time?", "context": image},
+        {"question": "What is the gross weight?", "context": image},
+        {"question": "What is the tare weight?", "context": image},
+        {"question": "What is the net weight?", "context": image},
+        {"question": "What is the moisture (moist) percentage?", "context": image},
+        {"question": "What is the damage percentage?", "context": image},
+        {"question": "What is the gross units?", "context": image},
+        {"question": "What is the dock units?", "context": image},
+        {"question": "What is the comment?", "context": image},
+        {"question": "What is the assembly number?", "context": image},
+    ]
+    
+    # Use the model to answer each question
+    results = [doc_qa(q["question"], q["context"]) for q in questions]
+
+    # Extract answers from the results
+    ticket_number = results[0][0]['answer']
+    date = results[1][0]['answer']
+    time = results[2][0]['answer']
+    gross_weight = results[3][0]['answer']
+    tare_weight = results[4][0]['answer']
+    net_weight = results[5][0]['answer']
+    moisture = results[6][0]['answer']
+    damage = results[7][0]['answer']
+    gross_units = results[8][0]['answer']
+    dock_units = results[9][0]['answer']
+    comment = results[10][0]['answer']
+    assembly_number = results[11][0]['answer']
+    
+    # Create a structured format (like a table) using pandas
+    data = {
+        "Ticket Number": [ticket_number],
+        "Assembly Number": [assembly_number],
+        "Date": [date],
+        "Time": [time],
+        "Gross Weight": [gross_weight],
+        "Tare Weight": [tare_weight],
+        "Net Weight": [net_weight],
+        "Moisture": [moisture],
+        "Damage": [damage],
+        "Gross Units": [gross_units],
+        "Dock Units": [dock_units],
+        "Comment": [comment],
+    }
+    df = pd.DataFrame(data)
+    
+    return df
+    
+    
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.Interface(
+    fn=parse_ticket_image, 
+    inputs=[gr.Image(label= "Upload your Grain Scale Ticket", type="pil")],
+    outputs=[gr.Dataframe(headers=["Field", "Value"], label="Extracted Grain Scale Data"],
+)
+
+
+if __name__ == "__main__":
+    demo.launch()