Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+from datasets import load_dataset, Dataset
+import pandas as pd
+
+# Load the dataset
+ds = load_dataset("bitext/Bitext-customer-support-llm-chatbot-training-dataset")
+
+# Convert the dataset to a pandas DataFrame
+df = ds['train'].to_pandas()
+
+# Define labels based on your intent categories
+label2id = {label: idx for idx, label in enumerate(df['intent'].unique())}
+id2label = {idx: label for label, idx in label2id.items()}
+
+# Encode labels
+df['label'] = df['intent'].map(label2id)
+
+# Ensure 'instruction', 'label', 'intent', and 'response' columns are included
+df = df[['instruction', 'label', 'intent', 'response']]
+
+# Load the tokenizer and model
+tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
+model = RobertaForSequenceClassification.from_pretrained('path_to_your_saved_model_directory')
+
+# Ensure the model is in evaluation mode
+model.eval()
+
+# Function to get the predicted intent and response
+def get_intent_and_response(instruction):
+    # Tokenize the input instruction
+    inputs = tokenizer(instruction, return_tensors="pt", truncation=True, padding='max_length', max_length=128)
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_label_id = torch.argmax(logits, dim=1).item()
+    
+    # Decode the predicted label to get the intent
+    predicted_intent = id2label[predicted_label_id]
+    
+    # Fetch the appropriate response based on the predicted intent
+    response = df[df['intent'] == predicted_intent].iloc[0]['response']
+    
+    return predicted_intent, response
+
+# Streamlit app setup
+st.title("Customer Support Chatbot")
+st.write("Ask a question, and I'll do my best to help you.")
+
+instruction = st.text_input("You:")
+
+if st.button("Submit"):
+    if instruction:
+        predicted_intent, response = get_intent_and_response(instruction)
+        st.write(f"**Predicted Intent:** {predicted_intent}")
+        st.write(f"**Assistant:** {response}")
+    else:
+        st.write("Please enter an instruction.")
+
+if st.button("Exit"):
+    st.write("Exiting the chat.")