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| # Import dependencies | |
| import gradio as gr | |
| # from llama_index import GPTVectorStoreIndex | |
| # from query_data import get_chain | |
| from langchain.chat_models import ChatOpenAI | |
| # create the OpenAI chatbot | |
| chatbot = ChatOpenAI() | |
| # define the function to generate the chatbot response | |
| def generate_response(text): | |
| response = chatbot.generate_response(text) | |
| return response | |
| # create the Gradio interface | |
| interface = gr.Interface( | |
| fn=generate_response, | |
| inputs=gr.inputs.Textbox(label="Input Text"), | |
| outputs=gr.outputs.Textbox(label="Output Text") | |
| ) | |
| # launch the interface | |
| interface.launch() | |
| #from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate | |
| #from langchain.memory import ConversationBufferWindowMemory | |
| #template = | |
| """You are a brilliant and empathic counselor. You encourage human to share feelings. | |
| You provide resources when appropriate or if asked. | |
| {history} | |
| Human: {human_input} | |
| Assistant:""" | |
| """prompt = PromptTemplate(input_variables=["history", "human_input"], template=template) | |
| chatgpt_chain = LLMChain( | |
| llm=OpenAI(temperature=0.8), | |
| prompt=prompt, | |
| verbose=False, | |
| memory=ConversationBufferWindowMemory(k=2), | |
| ) | |
| output = chatgpt_chain.predict( | |
| human_input= | |
| iface = gr.Interface(fn=get_response, inputs="text", outputs="text")""" | |
| """chat = ChatOpenAI(temperature=0) | |
| template = "You are a brilliant and empathic counselor. You encourage to share and provide resources when asked." | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "{text}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| chain = LLMChain(llm=chat, prompt=chat_prompt) | |
| chain.run(text="I feel lonely.")""" | |
| # Execute the chat functionality. | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.HTML("<center><h2>Omdena AI Chatbot For Mental Health and Wellbeing</h2></center>") | |
| gr.HTML("WELCOME<br>" | |
| "I am an AI ChatBot and I am here to assist you with whatever is bothering you. " | |
| "Our conversation is strictly confidential and I will not remember it when you come back another time." | |
| ) | |
| chatbot = gr.Chatbot() | |
| chat_message = gr.Textbox(label="What would you like to chat about?") | |
| response = gr.Textbox """ | |
| # define function to get chatbot response | |
| """ def get_response(text): | |
| response = agent.run(text) | |
| return response """ | |
| """ def respond(chat_message, chat_history): | |
| response = get_chain(chat_message, chat_history) | |
| chat_history.append((chat_message, response)) | |
| return "", chat_history """ | |
| """ with gr.Row(): | |
| send = gr.Button(value="Send").style(full_width=False) | |
| clear = gr.Button(value="Clear Chat").style(full_width=False) | |
| gr.Examples( | |
| examples=[ | |
| "I feel lonely", | |
| "I'm having problems at home", | |
| "I am looking for some resources", | |
| ], | |
| inputs=chat_message | |
| ) | |
| send.click(get_response(chat_message)) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) | |
| """ |