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| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| def create_roberta_chatbot_tab(): | |
| # Load pre-trained model and tokenizer | |
| model_name = "roberta-base" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| # Create a text classification pipeline | |
| classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) | |
| # Define response generation function | |
| def generate_response(input_text): | |
| # Classify the input text | |
| result = classifier(input_text)[0] | |
| label = result['label'] | |
| score = result['score'] | |
| # Map the classification result to a response | |
| responses = { | |
| "LABEL_0": "I understand you might be going through a difficult time. Remember, it's okay to seek help when you need it.", | |
| "LABEL_1": "Your feelings are valid. Have you considered talking to a mental health professional about this?", | |
| "LABEL_2": "Taking care of your mental health is crucial. Small steps like regular exercise and good sleep can make a big difference.", | |
| "LABEL_3": "It sounds like you're dealing with a lot. Remember, you're not alone in this journey.", | |
| "LABEL_4": "I hear you. Coping with mental health challenges can be tough. Have you tried any relaxation techniques like deep breathing or meditation?" | |
| } | |
| return responses.get(label, "I'm here to listen and support you. Could you tell me more about how you're feeling?") | |
| # Define chatbot function for Gradio | |
| def chatbot(message, history): | |
| response = generate_response(message) | |
| return response | |
| # Create Gradio interface | |
| iface = gr.ChatInterface( | |
| fn=chatbot, | |
| # title="Mental Health Support Chatbot (RoBERTa)", | |
| ) | |
| return iface |