stream app

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import pickle
+import pandas as pd
+from transformers import RobertaTokenizerFast, RobertaModel
+
+# -------------------------------
+# Load label mappings
+with open("label_mappings.pkl", "rb") as f:
+    label_mappings = pickle.load(f)
+
+label_to_team = label_mappings.get("label_to_team", {})
+label_to_email = label_mappings.get("label_to_email", {})
+
+# -------------------------------
+# Load tokenizer
+tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
+
+# -------------------------------
+# Define RoBERTa Model for multi-task classification
+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
+
+# -------------------------------
+# Initialize model and load checkpoint
+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"])
+    return "\n".join(predictions), df
+
+# -------------------------------
+# Streamlit UI
+st.title("AI Solution for Defect Ticket Classification")
+st.markdown("""
+**Supports:** Multi-line text input & CSV upload.  
+**Output:** Text results & downloadable CSV file.  
+**Model:** Fine-tuned **RoBERTa** for classification.
+""")
+
+# Choose input method
+option = st.radio("📝 Choose Input Method", ["Enter Text", "Upload CSV"])
+
+if option == "Enter Text":
+    text_input = st.text_area(
+        "Enter Ticket Description/Comment/Summary (One per line)", 
+        placeholder="Example:\n - Database performance issue\n - Login fails for admin users..."
+    )
+    descriptions = [line.strip() for line in text_input.split("\n") if line.strip()]
+else:
+    file_input = st.file_uploader("Upload CSV", type=["csv"])
+    descriptions = []
+    if file_input is not None:
+        df_input = pd.read_csv(file_input)
+        if "Description" not in df_input.columns:
+            st.error("⚠️ Error: CSV must contain a 'Description' column.")
+        else:
+            descriptions = df_input["Description"].dropna().tolist()
+
+# Trigger prediction when the button is clicked
+if st.button("PREDICT"):
+    if not descriptions:
+        st.error("⚠️ Please provide valid input.")
+    else:
+        with st.spinner("Predicting..."):
+            results, df_results = predict_tickets(descriptions)
+        st.markdown("## Prediction Results")
+        st.text(results)
+        csv_data = df_results.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="📥 Download Predictions CSV",
+            data=csv_data,
+            file_name="ticket-predictions.csv",
+            mime="text/csv"
+        )
+
+# Clear button: simply reloads the app
+if st.button("CLEAR"):
+    st.experimental_rerun()
+
+st.markdown("---")
+st.markdown(
+    "<p style='text-align: center;color: gray;'>Developed by NYP student @ Min Thein Win: Student ID: 3907578Y</p>", 
+    unsafe_allow_html=True
+)

label_mappings.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:db90d041f35923a582c2bd4e795ca06632b8b23e1e9eaab6622844ccc27c47c7
+size 315

requirements.txt

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+streamlit==1.24.0
+torch==2.0.0
+transformers==4.27.0
+pandas==1.5.3

ticket_classification_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4a507d772bd1ffe5ae6878e31ce1b86a479b69f204d682f775544098a6fbdb0
+size 498701064