update app file and extra other file

app.py

@@ -1,7 +1,140 @@
 import gradio as gr
+import torch
+import torch.nn as nn
+import pickle
+import pandas as pd
+from transformers import RobertaTokenizerFast, RobertaModel
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load label mappings
+with open("label_mappings.pkl", "rb") as f:
+    label_mappings = pickle.load(f)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+label_to_team = label_mappings.get("label_to_team", {})
+label_to_email = label_mappings.get("label_to_email", {})
+
+# Load the tokenizer
+tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
+
+# Define RoBERTa Model
+class RoBertaClassifier(nn.Module):
+    def __init__(self, num_teams, num_emails):
+        super(RoBertaClassifier, self).__init__()
+        self.roberta = RobertaModel.from_pretrained("roberta-base")
+        self.team_classifier = nn.Linear(self.roberta.config.hidden_size, num_teams)
+        self.email_classifier = nn.Linear(self.roberta.config.hidden_size, num_emails)
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
+        cls_output = outputs.last_hidden_state[:, 0, :]
+
+        team_logits = self.team_classifier(cls_output)
+        email_logits = self.email_classifier(cls_output)
+
+        return team_logits, email_logits
+
+# Load Model
+num_teams = len(label_to_team)
+num_emails = len(label_to_email)
+model = RoBertaClassifier(num_teams, num_emails)
+checkpoint = torch.load("ticket_classification_model.pth", map_location=torch.device("cpu"))
+filtered_checkpoint = {k: v for k, v in checkpoint.items() if k in model.state_dict()}
+model.load_state_dict(filtered_checkpoint, strict=False)
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+model.to(device)
+model.eval()
+
+# Prediction Function
+def predict_tickets(ticket_descriptions):
+    predictions = []
+    csv_data = []
+    for idx, description in enumerate(ticket_descriptions, start=1):
+        inputs = tokenizer(description, return_tensors="pt", truncation=True, padding="max_length", max_length=128).to(device)
+        with torch.no_grad():
+            team_logits, email_logits = model(inputs.input_ids, inputs.attention_mask)
+        predicted_team_index = team_logits.argmax(dim=-1).cpu().item()
+        predicted_email_index = email_logits.argmax(dim=-1).cpu().item()
+        predicted_team = label_to_team.get(predicted_team_index, "Unknown Team")
+        predicted_email = label_to_email.get(predicted_email_index, "Unknown Email")
+        predictions.append(f"**{idx}. {description}**\n   - **Assigned Team:** {predicted_team}\n   - **Team Email:** {predicted_email}\n")
+        csv_data.append([idx, description, predicted_team, predicted_email])
+
+    df = pd.DataFrame(csv_data, columns=["Index", "Description", "Assigned Team", "Team Email"])
+    csv_file = "ticket-predictions.csv"
+    df.to_csv(csv_file, index=False)
+    return "\n".join(predictions), csv_file
+
+# Gradio Functions
+def gradio_predict(option, text_input, file_input):
+    if option == "Enter Text":
+        descriptions = text_input.split("\n")
+        descriptions = [desc.strip() for desc in descriptions if desc.strip()]
+    elif option == "Upload CSV" and file_input is not None:
+        df = pd.read_csv(file_input)
+        if "Description" not in df.columns:
+            return "⚠️ Error: CSV must contain a 'Description' column.", None
+        descriptions = df["Description"].tolist()
+    else:
+        return "⚠️ Please provide input.", None
+
+    results, csv_file = predict_tickets(descriptions)
+    return results, csv_file
+
+def clear_inputs():
+    return "Enter Text", "", None, "", None
+
+# Gradio App UI
+with gr.Blocks(css=".gradio-container {max-width: 1100px; margin: auto;}") as app:
+    gr.Markdown(
+        """
+        # Multi-Ticket AI Classification System
+
+        **Supports:** Multi-line text input & CSV upload.  
+        **Output:** Text results & downloadable CSV file.  
+        **Model:** Fine-tuned **RoBERTa** for classification.  
+
+        Enter ticket Description/Comment/Summary or upload a **CSV file** to predict Assigned Teams & Team Emails.
+        
+        """,
+        elem_id="title"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            option = gr.Radio(["Enter Text", "Upload CSV"], label="📝 Choose Input Method", value="Enter Text")
+
+            text_input = gr.Textbox(
+                label="Enter Ticket Description/Comment/Summary (One per line)", 
+                lines=6, 
+                placeholder="Example:\n - Database performance issue\n - Login fails for admin users..."
+            )
+
+            file_input = gr.File(label="📂 Upload CSV (Optional)", type="filepath", visible=False)
+
+        with gr.Column(scale=1):
+            gr.Markdown("## Prediction Results")  # **Title for Prediction Results**
+            results_output = gr.Markdown(elem_id="results-box", visible=True)
+            download_csv = gr.File(label="📥 Download Predictions CSV", interactive=False)
+
+    with gr.Row():
+        predict_btn = gr.Button("PREDICT", variant="primary")
+        clear_btn = gr.Button("CLEAR", variant="secondary")
+
+    # Logic for Showing/ Hiding Input Fields
+    def toggle_input(selected_option):
+        return gr.update(visible=(selected_option == "Enter Text")), gr.update(visible=(selected_option == "Upload CSV"))
+
+    option.change(fn=toggle_input, inputs=[option], outputs=[text_input, file_input])
+
+    predict_btn.click(fn=gradio_predict, inputs=[option, text_input, file_input], outputs=[results_output, download_csv])
+    clear_btn.click(fn=clear_inputs, inputs=[], outputs=[option, text_input, file_input, results_output, download_csv])
+
+     # Footer view
+    gr.Markdown("---")
+    gr.Markdown(
+        "<p style='text-align: center;color: gray;'>"
+        "Developed by NYP student @ Min Thein Win: Student ID: 3907578Y</p>"
+    )
+
+# Launch App
+app.launch(share=True)

label_mappings.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db90d041f35923a582c2bd4e795ca06632b8b23e1e9eaab6622844ccc27c47c7
+size 315

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+torch
+torchvision
+torchaudio
+transformers
+pandas

ticket_classification_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a33510b236c5dea7a1c00276dd933b302a10711c2a411fa059ca81e5651de030
+size 498701000