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import gradio as gr
from gemini_api import model_api, sentiment, category, ord_num
cust_qry_resp = {"senti":"", "cat":"", "num":""}
#********* UI Code ***********#
with gr.Blocks(title="Customer Support Assistant",
analytics_enabled=False) as app:
gr.Markdown("Customer Support Assistant")
# Inputs from user
with gr.Row():
cust_qry = gr.Textbox(lines=5, type="text", label="Customer Query")
btn_cust_qry = gr.Button("Analyze Query")
# Model Output
@gr.render(inputs=[cust_qry], triggers=[btn_cust_qry.click])
# Function for prediction
def invoke_model(user_input):
if len(user_input) == 0:
gr.Markdown("## No Customer Query Provided")
else:
senti = model_api(user_input, sentiment)
cat = model_api(user_input, category)
num = model_api(user_input, ord_num)
# Output response
with gr.Row():
gr.Textbox(lines=1, type="text", label="Customer Sentiment", value=senti)
with gr.Row():
gr.Textbox(lines=1, type="text", label="Order Category", value=cat)
with gr.Row():
gr.Textbox(lines=1, type="text", label="Order Number", value=num)
if num != "Order Number not provided.":
btn_ord_det = gr.Button("Fetch Order Details")
if senti == "NEGATIVE" and num == "Order Number not provided.":
gr.Textbox(lines=1, type="text", label="Next Step", value="Ask Order Number")
if __name__ == "__main__":
app.launch(server_name="0.0.0.0") |