| import gradio as gr | |
| from transformers import pipeline | |
| from gpt4all import GPT4All | |
| model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf") | |
| model_name = "distilbert/distilbert-base-uncased-finetuned-sst-2-english" | |
| sentiment_analysis = pipeline("text-classification", model=model_name) | |
| def get_sentiment(text): | |
| analysed_text = str(sentiment_analysis (text)[0]["label"]) | |
| return analysed_text | |
| def generate_prompt(user_input): | |
| sentiment = get_sentiment(user_input) | |
| if sentiment == 'POSITIVE': | |
| response = f"User is happy and said: {user_input}. Tell the user that it is good to know that they are happy and also write a two line poetry to celebrate their happiness." | |
| else: | |
| response = f"User is sad and said: {user_input}. Respond with a comforting message and tell them to share their issues if they would like, also write a poem to cheer them on." | |
| return response | |
| def chatbot_response(input_text): | |
| text_prompt = generate_prompt(input_text) | |
| tokens = [] | |
| with model.chat_session() as session: | |
| for token in model.generate(text_prompt, streaming=True): | |
| tokens.append(token) | |
| response = ''.join(tokens) | |
| return response | |
| iface = gr.Interface( | |
| fn=chatbot_response, | |
| inputs=gr.components.Textbox(lines=2, placeholder="......."), | |
| outputs="text", | |
| ) | |
| iface.launch(debug=True) |