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| import pandas as pd | |
| import openai | |
| import faiss | |
| import numpy as np | |
| import time | |
| import os | |
| import pickle | |
| import gradio as gr | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from io import StringIO | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| def create_and_save_faiss_index(questions, embedding_model, index_file, embedding_file): | |
| question_embeddings = embedding_model.embed_documents(questions) | |
| faiss_index = faiss.IndexFlatL2(len(question_embeddings[0])) | |
| faiss_index.add(np.array(question_embeddings)) | |
| faiss.write_index(faiss_index, index_file) | |
| with open(embedding_file, 'wb') as f: | |
| pickle.dump(question_embeddings, f) | |
| return faiss_index, question_embeddings | |
| def load_faiss_index(index_file, embedding_file): | |
| faiss_index = faiss.read_index(index_file) | |
| with open(embedding_file, 'rb') as f: | |
| question_embeddings = pickle.load(f) | |
| return faiss_index, question_embeddings | |
| def retrieve_answer(question, faiss_index, embedding_model, answers, threshold=0.8): | |
| question_embedding = embedding_model.embed_query(question) | |
| distances, indices = faiss_index.search(np.array([question_embedding]), k=1) | |
| closest_distance = distances[0][0] | |
| closest_index = indices[0][0] | |
| print(f"closest_distance: {closest_distance}") | |
| if closest_distance > threshold: | |
| return "No good match found in dataset. Using GPT-4o-mini to generate an answer." | |
| else: | |
| return answers[closest_index] | |
| def ask_openai_gpt4(question): | |
| response = openai.chat.completions.create( | |
| messages=[ | |
| {"role": "user", "content": f"Answer the following medical question: {question}"} | |
| ], | |
| model="gpt-4o-mini", | |
| max_tokens=150 | |
| ) | |
| return response.choices[0].message.content | |
| def respond(message, history, system_message, max_tokens, temperature, top_p): | |
| log_output = StringIO() | |
| start_time = time.time() | |
| if os.path.exists('faiss.index') and os.path.exists('embeddings.pkl'): | |
| log_output.write("Loading FAISS index from disk...\n") | |
| faiss_index, question_embeddings = load_faiss_index('faiss.index', 'embeddings.pkl') | |
| else: | |
| log_output.write("Creating and saving FAISS index...\n") | |
| df = pd.read_csv("medquad.csv") | |
| questions = df['question'].tolist() | |
| answers = df['answer'].tolist() | |
| embedding_model = OpenAIEmbeddings(openai_api_key=openai.api_key) | |
| faiss_index, question_embeddings = create_and_save_faiss_index(questions, embedding_model, 'faiss.index', 'embeddings.pkl') | |
| messages = [{"role": "system", "content": system_message}] | |
| for user_message, bot_response in history: | |
| messages.append({"role": "user", "content": user_message}) | |
| if bot_response: | |
| messages.append({"role": "assistant", "content": bot_response}) | |
| user_message = message | |
| messages.append({"role": "user", "content": user_message}) | |
| response_text = retrieve_answer(user_message, faiss_index, OpenAIEmbeddings(openai_api_key=openai.api_key), answers=["..."], threshold=0.8) | |
| if response_text == "No good match found in dataset. Using GPT-4o-mini to generate an answer.": | |
| log_output.write("No good match found in dataset. Using GPT-4o-mini to generate an answer.\n") | |
| response_text = ask_openai_gpt4(user_message) | |
| # Stop the timer and calculate response time | |
| end_time = time.time() | |
| response_time = end_time - start_time # Time in seconds | |
| # Yield the response with the logs and response time | |
| yield response_text, f"Response time: {response_time:.4f} seconds", log_output.getvalue() | |
| # Gradio ChatInterface with additional inputs for model settings and response time | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") | |
| ], | |
| title="Medical Chatbot with Customizable Parameters and Response Time", | |
| description="A chatbot with customizable parameters using FAISS for quick responses or fallback to GPT-4 when no relevant answer is found. Response time is also tracked." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |