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| from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper | |
| from langchain import OpenAI | |
| import gradio as gr | |
| import sys | |
| import os | |
| import datetime | |
| os.environ["OPENAI_API_KEY"] = 'SECRET' | |
| def construct_index(directory_path): | |
| max_input_size = 4096 | |
| num_outputs = 512 | |
| max_chunk_overlap = 20 | |
| chunk_size_limit = 600 | |
| prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) | |
| prompt = "You are John Haynes, an experienced mediator who lived from 1923 to 1999. You are sometimes called the grandfather of mediation practice because you helped train many mediators during your life. In addition to providing advice on mediation practice you will also finish your answers by asking deep, philosophical and insightful questions based on the topic presented. \n\n" | |
| llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs)) | |
| documents = SimpleDirectoryReader(directory_path).load_data() | |
| index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) | |
| index.save_to_disk('index.json') | |
| return index | |
| def chatbot(input_text, mentioned_person='Mediation teacher John Haynes'): | |
| index = GPTSimpleVectorIndex.load_from_disk('index.json') | |
| prompt = f"{mentioned_person}: {input_text}\n\n At the end of your answer, if you think appropriate, please ask a provocative question. Start it with a polite phrase such as - I wonder what you think...-." | |
| response = index.query(prompt, response_mode="compact") | |
| # Check if response includes a question mark | |
| if "?" not in response.response: | |
| # If response does not include a question, add one | |
| response.response += "\n\nWhat are your thoughts on this?" | |
| # Save chat log | |
| current_time = datetime.datetime.now() | |
| current_time_str = current_time.strftime("%Y-%m-%d_%H-%M-%S") | |
| chat_log_filename = f"{current_time_str}.txt" | |
| chat_log_filepath = os.path.join('docs/chathistory', chat_log_filename) | |
| with open(chat_log_filepath, "w") as f: | |
| f.write(f"Chat started at {current_time_str}\n\n") | |
| f.write(f"User: {input_text}\n") | |
| f.write(f"Chatbot: {response.response}\n\n") | |
| return response.response | |
| with open("docs/about/descript.txt", "r") as f: | |
| description = f.read() | |
| iface = gr.Interface(fn=chatbot, | |
| inputs=gr.inputs.Textbox(lines=5, label="Enter your question"), | |
| outputs=gr.outputs.Textbox(label="Chatbot Response"), | |
| title="AI Chatbot trained on J. Haynes mediation material, v0.1", | |
| description=description) | |
| index = construct_index("docs") | |
| iface.launch(share=True) | |