Updated File

app.py

@@ -1,256 +1,257 @@
-"""
-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()
+"""
+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
+import numpy as np
+
+# 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()