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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 | |
| from huggingface_hub import hf_hub_download | |
| from huggingface_hub import login | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| hf_token = os.getenv("HF_TOKEN") | |
| login(token=hf_token) | |
| def load_embeddings_and_faiss(): | |
| embeddings_path = hf_hub_download(repo_id="chukbert/embedding-faq-medquad", filename="embeddings.pkl",repo_type="dataset", token=hf_token) | |
| faiss_index_path = hf_hub_download(repo_id="chukbert/embedding-faq-medquad", filename="faiss.index",repo_type="dataset", token=hf_token) | |
| faiss_index = faiss.read_index(faiss_index_path) | |
| with open(embeddings_path, 'rb') as f: | |
| question_embeddings = pickle.load(f) | |
| return faiss_index, question_embeddings | |
| def retrieve_answer(question, faiss_index, embedding_model, answers, log_output, threshold=0.2): | |
| 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] | |
| log_output.write(f"closest_distance: {closest_distance}") | |
| if closest_distance > threshold: | |
| return "No good match found in dataset. Using GPT-4o-mini to generate an answer." | |
| 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 chatbot(user_input): | |
| log_output = StringIO() # To capture logs | |
| faiss_index, question_embeddings = load_embeddings_and_faiss() | |
| embedding_model = OpenAIEmbeddings(openai_api_key=openai.api_key) | |
| start_time = time.time() # Start timer | |
| log_output.write("Retrieving answer from FAISS...\n") | |
| response_text = retrieve_answer(user_input, faiss_index, embedding_model, answers, log_output, threshold=0.3) | |
| 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_input) | |
| end_time = time.time() # End timer | |
| response_time = end_time - start_time # Calculate response time | |
| # Log the final response time | |
| # Return the chatbot response, response time, and log | |
| return response_text, f"Response time: {response_time:.4f} seconds", log_output.getvalue() | |
| # Simplified Gradio interface with response, response time, and logs | |
| demo = gr.Interface( | |
| fn=chatbot, # Main chatbot function | |
| inputs="text", # User input: single text field | |
| outputs=[ | |
| gr.Textbox(label="Chatbot Response"), # Named output for the chatbot response | |
| gr.Textbox(label="Response Time"), # Named output for the response time | |
| gr.Textbox(label="Logs") # Logs | |
| ], | |
| title="Medical Chatbot with Custom Knowledge About Medical FAQ", | |
| description="A chatbot with custom knowledge using FAISS for quick responses or fallback to GPT-4o-mini when no relevant answer is found. Response time is also tracked." | |
| ) | |
| if __name__ == "__main__": | |
| # Load dataset | |
| df = pd.read_csv("medquad.csv") | |
| questions = df['question'].tolist() | |
| answers = df['answer'].tolist() | |
| print(f"Loaded questions and answers. Number of questions: {len(questions)}, Number of answers: {len(answers)}") | |
| demo.launch() |