Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import pandas as pd
+
+
+gr.Markdown(
+    """
+    <style>
+        .center-btn button {
+            margin-left: auto;
+            margin-right: auto;
+            display: block;
+        }
+    </style>
+    """
+)
+
+
+# Load model and tokenizer
+model_name = "ale-dp/xlm-roberta-email-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Label map
+label_map = {
+    0: 'Billing and Payments',
+    1: 'Customer Service',
+    2: 'General Inquiry',
+    3: 'Human Resources',
+    4: 'IT Support',
+    5: 'Product Support',
+    6: 'Returns and Exchanges',
+    7: 'Sales and Pre-Sales',
+    8: 'Service Outages and Maintenance',
+    9: 'Technical Support'
+}
+
+# Prediction function
+def classify_email_with_probs(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits[0]
+    probs = torch.nn.functional.softmax(logits, dim=0)
+
+    prob_dict = {label_map[i]: round(float(probs[i]) * 100, 2) for i in range(len(probs))}
+    sorted_probs = dict(sorted(prob_dict.items(), key=lambda item: item[1], reverse=True))
+    df = pd.DataFrame(sorted_probs.items(), columns=["Category", "Confidence (%)"])
+    top_label = df.iloc[0]["Category"]
+    return top_label, df
+
+# Sample emails
+examples = [
+    "Hello, I recently purchased a pair of headphones from your online store (Order #48392) and unfortunately, they arrived damaged. The left earcup is completely detached and the sound is distorted. I’d like to request a return or exchange. Please let me know the steps I need to follow and whether I need to ship the item back first. Thank you for your assistance.",
+    "Dear Customer Support Team,\n\nI hope this message reaches you well. I am reaching out to request detailed billing details and payment options for a QuickBooks Online subscription. Specifically, I am interested in understanding the available plans, their pricing structures, and any tailored options for institutional clients within the financial services industry.",
+    "Hello, I’m reaching out on behalf of a mid-sized retail company interested in your cloud-based inventory solution. We’re currently evaluating vendors and would appreciate a demo of your platform, along with pricing tiers for teams of 50+ users. Please let me know your availability this week for a call.",
+    "Currently facing sporadic connectivity difficulties with the cloud-native SaaS system. The suspected reason appears to be linked to orchestration resource distribution within Kubernetes-managed microservices. After restarting the affected services and examining deployment logs, the issue continues. Further investigation and escalation are required to resolve this matter swiftly."
+]
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 📬 Email Ticket Classifier")
+    gr.Markdown("Classify emails into support categories using XLM-RoBERTa. See top prediction and full confidence breakdown.")
+
+    email_input = gr.Textbox(
+        lines=12,
+        label="Email Text",
+        placeholder="Paste your email here...",
+        elem_id="email_input"
+    )
+
+    with gr.Row():
+        submit_btn = gr.Button("Classify", variant="primary", elem_classes="center-btn")
+
+    gr.Markdown("<br><br>")
+
+    gr.Markdown("### Examples:")
+
+    with gr.Column():
+        for example in examples:
+            gr.Button(example).click(fn=lambda x=example: x, outputs=email_input)
+
+    
+    top_label = gr.Label(label="Predicted Category")
+    prob_table = gr.Dataframe(
+        headers=["Category", "Confidence (%)"],
+        label="Confidence Breakdown",
+        datatype=["str", "number"],
+        row_count=10
+    )
+
+    submit_btn.click(fn=classify_email_with_probs, inputs=email_input, outputs=[top_label, prob_table])
+
+demo.launch()