Create app.py

app.py

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+import gradio as gr
+import onnxruntime as ort
+import numpy as np
+from tokenizers import Tokenizer
+from huggingface_hub import hf_hub_download
+
+# Configuration parameters
+MAX_LEN = 256
+
+# Hardcoded description value
+DESCRIPTION_TEXT = (
+    "I am raising this case to report a severe and ongoing issue of vermin infestation, "
+    "specifically rats and mice, in my residential area. The problem appears to be directly linked "
+)
+
+# Define possible choices for each field
+status_choices = ["Assess & Assign", "Generate Letter", "Site Inspection"]
+category_choices = ["Litter and Nuisance"]
+request_reason_choices = ["Nuisance"]
+request_sub_reason_choices = ["Animals"]
+additional_reason_choices = ["Vermin, Rats and Mice", "Dog", "Cat", "Horse"]
+notification_method_choices = ["No Notification", "Email", "Phone"]
+inspection_performed_choices = ["Yes", "No"]
+letter_sent_choices = ["Yes", "No"]
+
+# Download the ONNX model and tokenizer from Hugging Face Hub
+onnx_model_path = hf_hub_download(
+    repo_id="iimran/Case-Next-Best-Action-Classifier", filename="moodlens.onnx"
+)
+tokenizer_path = hf_hub_download(
+    repo_id="iimran/Case-Next-Best-Action-Classifier", filename="train_bpe_tokenizer.json"
+)
+
+# Load the tokenizer and ONNX model once outside the function for efficiency.
+tokenizer = Tokenizer.from_file(tokenizer_path)
+session = ort.InferenceSession(onnx_model_path)
+input_name = session.get_inputs()[0].name
+output_name = session.get_outputs()[0].name
+
+def predict_action(status, category, request_reason, request_sub_reason,
+                   additional_reason, notification_method, inspection_performed, letter_sent):
+    # Combine fields into one input string.
+    fields = [
+        status,
+        category,
+        request_reason,
+        request_sub_reason,
+        additional_reason,
+        notification_method,
+        DESCRIPTION_TEXT,
+        inspection_performed,
+        letter_sent
+    ]
+    sample_text = " ".join(fields)
+    
+    # Tokenize and pad the input
+    encoding = tokenizer.encode(sample_text)
+    ids = encoding.ids[:MAX_LEN]
+    padding = [0] * (MAX_LEN - len(ids))
+    input_ids = np.array([ids + padding], dtype=np.int64)
+    
+    # Run inference
+    outputs = session.run([output_name], {input_name: input_ids})
+    predicted_class = np.argmax(outputs[0], axis=1)[0]
+    
+    # Map predicted index to the actual action labels
+    label_names = [
+        "Assign Case Officer",
+        "Generate Letter and Send By Post",
+        "Generate Letter and Send Email",
+        "Generate Letter and Send SMS",
+        "Schedule Inspection",
+        "Send Feedback Survey"
+    ]
+    predicted_label = label_names[predicted_class]
+    return predicted_label
+
+# Create the Gradio Interface using the updated API.
+demo = gr.Interface(
+    fn=predict_action,
+    inputs=[
+        gr.Dropdown(choices=status_choices, label="Status"),
+        gr.Dropdown(choices=category_choices, label="Category"),
+        gr.Dropdown(choices=request_reason_choices, label="Request Reason"),
+        gr.Dropdown(choices=request_sub_reason_choices, label="Request Sub Reason"),
+        gr.Dropdown(choices=additional_reason_choices, label="Additional Reason"),
+        gr.Dropdown(choices=notification_method_choices, label="Notification Method"),
+        gr.Dropdown(choices=inspection_performed_choices, label="Inspection Performed"),
+        gr.Dropdown(choices=letter_sent_choices, label="Letter Sent")
+    ],
+    outputs=gr.Textbox(label="Predicted Action"),
+    title="MoodleLens Action Predictor",
+    description="Select values from the dropdowns. The description field is fixed."
+)
+
+demo.launch()