Program Files are Added.

README.md

@@ -1,12 +1,25 @@
----
-title: Ticket Classifier
-emoji: 📉
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: IT Ticket Classifier
+emoji: 🎫
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# IT Service Desk Ticket Classifier
+
+Classify IT support tickets into 12 categories using DistilBERT.
+
+## Categories
+- Access Management, Backup, Database, Email
+- General Inquiry, Hardware, Network, Other
+- Printing, Security, Software, Storage
+
+## Model
+- **Base**: DistilBERT (distilbert-base-uncased)
+- **Training**: 5,760 IT support tickets
+- **Architecture**: DistilBERT + Classifier head

app.py

@@ -0,0 +1,256 @@
+"""
+IT Ticket Classifier - HuggingFace Spaces App
+Gradio interface for classifying IT support tickets
+"""
+
+import gradio as gr
+import torch
+import torch.nn as nn
+from transformers import DistilBertModel, AutoTokenizer
+from huggingface_hub import hf_hub_download
+import re
+import os
+
+# Configuration
+HF_REPO_ID = "TuShar2309/ticket-classifier"
+MODEL_FILENAME = "ticket_classifier.pt"
+
+CLASS_NAMES = [
+    "Access Management", "Backup", "Database", "Email", 
+    "General Inquiry", "Hardware", "Network", "Other",
+    "Printing", "Security", "Software", "Storage"
+]
+
+# Category descriptions for display
+CATEGORY_INFO = {
+    "Access Management": "🔐 Login, permissions, MFA, account issues",
+    "Backup": "💾 Backup and restore operations",
+    "Database": "🗄️ SQL, database connectivity, queries",
+    "Email": "📧 Outlook, calendar, mailbox issues",
+    "General Inquiry": "❓ How-to questions, policies",
+    "Hardware": "💻 Laptop, monitor, keyboard, mouse",
+    "Network": "🌐 WiFi, VPN, internet connectivity",
+    "Other": "📋 Miscellaneous requests",
+    "Printing": "🖨️ Printers, scanning, print queue",
+    "Security": "🔒 Threats, malware, security incidents",
+    "Software": "📦 Application issues, installations",
+    "Storage": "📁 OneDrive, SharePoint, file storage"
+}
+
+
+class TicketPreprocessor:
+    def __init__(self):
+        self._email = re.compile(r'\b[\w.-]+@[\w.-]+\.\w+\b')
+    
+    def clean(self, text):
+        return ' '.join(self._email.sub('[EMAIL]', str(text or '')).lower().split())
+    
+    def combine(self, subject, description):
+        return f"[SUBJECT] {self.clean(subject)} [SEP] [DESCRIPTION] {self.clean(description)}"
+
+
+class TicketClassifier(nn.Module):
+    def __init__(self, num_classes, model_name="distilbert-base-uncased", dropout=0.3):
+        super().__init__()
+        self.bert = DistilBertModel.from_pretrained(model_name)
+        self.classifier = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(768, 256),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(256, num_classes)
+        )
+    
+    def forward(self, input_ids, attention_mask):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        return self.classifier(outputs.last_hidden_state[:, 0, :])
+    
+    def predict_proba(self, input_ids, attention_mask):
+        logits = self.forward(input_ids, attention_mask)
+        return torch.softmax(logits, dim=-1)
+
+
+# Load model
+print("Loading model...")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Device: {device}")
+
+try:
+    model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=MODEL_FILENAME)
+    print(f"Model downloaded: {model_path}")
+    
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+    model = TicketClassifier(num_classes=len(CLASS_NAMES))
+    
+    checkpoint = torch.load(model_path, map_location=device)
+    if 'model_state_dict' in checkpoint:
+        model.load_state_dict(checkpoint['model_state_dict'])
+    else:
+        model.load_state_dict(checkpoint)
+    
+    model.to(device)
+    model.eval()
+    MODEL_LOADED = True
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    MODEL_LOADED = False
+
+preprocessor = TicketPreprocessor()
+
+
+def classify_ticket(subject, description):
+    """Classify a ticket and return results."""
+    if not subject and not description:
+        return "⚠️ Please enter a subject or description", "", ""
+    
+    if not MODEL_LOADED:
+        return "❌ Model not loaded", "", ""
+    
+    try:
+        # Preprocess and tokenize
+        combined = preprocessor.combine(subject, description)
+        inputs = tokenizer(
+            combined,
+            return_tensors="pt",
+            truncation=True,
+            max_length=256,
+            padding='max_length'
+        ).to(device)
+        
+        # Predict
+        with torch.no_grad():
+            probs = model.predict_proba(inputs['input_ids'], inputs['attention_mask'])[0]
+        
+        probs_np = probs.cpu().numpy()
+        top_indices = probs_np.argsort()[::-1]
+        
+        # Primary prediction
+        primary_idx = top_indices[0]
+        primary_cat = CLASS_NAMES[primary_idx]
+        primary_conf = probs_np[primary_idx] * 100
+        
+        # Status
+        if primary_conf >= 80:
+            status = "✅ **High Confidence** - Auto-route recommended"
+        elif primary_conf >= 60:
+            status = "⚠️ **Medium Confidence** - Review suggested"
+        else:
+            status = "🔍 **Low Confidence** - Human review required"
+        
+        # Format primary result
+        primary_result = f"""
+## {CATEGORY_INFO.get(primary_cat, primary_cat)}
+
+### Predicted Category: **{primary_cat}**
+### Confidence: **{primary_conf:.1f}%**
+
+{status}
+"""
+        
+        # Format alternatives
+        alternatives = "### Other Possibilities:\n\n"
+        for i in range(1, min(4, len(top_indices))):
+            idx = top_indices[i]
+            cat = CLASS_NAMES[idx]
+            conf = probs_np[idx] * 100
+            alternatives += f"- **{cat}**: {conf:.1f}%\n"
+        
+        # Confidence bar
+        conf_display = f"{'█' * int(primary_conf / 5)}{'░' * (20 - int(primary_conf / 5))} {primary_conf:.1f}%"
+        
+        return primary_result, alternatives, conf_display
+        
+    except Exception as e:
+        return f"❌ Error: {str(e)}", "", ""
+
+
+# Example tickets
+examples = [
+    ["VPN not connecting", "Cannot connect to corporate VPN from home, getting timeout error"],
+    ["Suspicious email received", "Got an email asking for my password, looks like phishing"],
+    ["Need SharePoint access", "Just joined the marketing team, need access to the team SharePoint"],
+    ["Laptop screen flickering", "My laptop screen has been flickering intermittently since yesterday"],
+    ["Outlook not receiving emails", "Haven't received any emails in Outlook for the past 3 hours"],
+    ["How to reset password", "What is the process to reset my Active Directory password?"],
+    ["Printer not working", "Print jobs stuck in queue and won't print"],
+    ["SQL query slow", "Database query that used to take 2 seconds now takes 10 minutes"],
+]
+
+
+# Create Gradio interface
+with gr.Blocks(
+    title="IT Ticket Classifier",
+    theme=gr.themes.Soft(primary_hue="green", secondary_hue="blue"),
+    css="""
+    .gradio-container { max-width: 900px !important; }
+    .primary-result { font-size: 1.2em; }
+    """
+) as demo:
+    gr.Markdown("""
+    # 🎫 IT Service Desk Ticket Classifier
+    
+    **Powered by DistilBERT** | Classifies tickets into 12 IT support categories
+    
+    Enter a ticket subject and description below to get the predicted category.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            subject_input = gr.Textbox(
+                label="📋 Ticket Subject",
+                placeholder="e.g., VPN not connecting",
+                lines=1
+            )
+            description_input = gr.Textbox(
+                label="📝 Ticket Description",
+                placeholder="e.g., Cannot connect to corporate VPN from home, getting timeout error after 30 seconds...",
+                lines=4
+            )
+            classify_btn = gr.Button("🔍 Classify Ticket", variant="primary", size="lg")
+        
+        with gr.Column(scale=1):
+            primary_output = gr.Markdown(label="Primary Prediction")
+            confidence_output = gr.Textbox(label="Confidence", interactive=False)
+            alternatives_output = gr.Markdown(label="Alternatives")
+    
+    classify_btn.click(
+        fn=classify_ticket,
+        inputs=[subject_input, description_input],
+        outputs=[primary_output, alternatives_output, confidence_output]
+    )
+    
+    gr.Examples(
+        examples=examples,
+        inputs=[subject_input, description_input],
+        outputs=[primary_output, alternatives_output, confidence_output],
+        fn=classify_ticket,
+        cache_examples=False
+    )
+    
+    gr.Markdown("""
+    ---
+    ### 📊 Supported Categories
+    
+    | Category | Description |
+    |----------|-------------|
+    | Access Management | Login, permissions, MFA |
+    | Backup | Backup and restore |
+    | Database | SQL, queries, DB issues |
+    | Email | Outlook, calendar |
+    | General Inquiry | How-to questions |
+    | Hardware | Devices, laptops |
+    | Network | WiFi, VPN, internet |
+    | Other | Miscellaneous |
+    | Printing | Printers, scanning |
+    | Security | Threats, incidents |
+    | Software | Applications |
+    | Storage | OneDrive, SharePoint |
+    
+    ---
+    **Model**: DistilBERT fine-tuned on 5,760 IT support tickets
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.44.0
+torch==2.1.0
+transformers==4.36.0
+huggingface_hub==0.20.0