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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModel | |
| from openai import OpenAI | |
| import os | |
| import numpy as np | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| # Load the NASA-specific bi-encoder model and tokenizer | |
| bi_encoder_model_name = "nasa-impact/nasa-smd-ibm-st-v2" | |
| bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name) | |
| bi_model = AutoModel.from_pretrained(bi_encoder_model_name) | |
| # Set up OpenAI client | |
| api_key = os.getenv('OPENAI_API_KEY') | |
| client = OpenAI(api_key=api_key) | |
| # Define a system message to introduce Exos | |
| system_message = "You are Exos, a helpful assistant specializing in Exoplanet research. Provide detailed and accurate responses related to Exoplanet research." | |
| def encode_text(text): | |
| inputs = bi_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128) | |
| outputs = bi_model(**inputs) | |
| return outputs.last_hidden_state.mean(dim=1).detach().numpy().flatten() # Ensure the output is 2D | |
| def retrieve_relevant_context(user_input, context_texts): | |
| user_embedding = encode_text(user_input).reshape(1, -1) | |
| context_embeddings = np.array([encode_text(text) for text in context_texts]) | |
| context_embeddings = context_embeddings.reshape(len(context_embeddings), -1) # Flatten each embedding | |
| similarities = cosine_similarity(user_embedding, context_embeddings).flatten() | |
| most_relevant_idx = np.argmax(similarities) | |
| return context_texts[most_relevant_idx] | |
| def generate_response(user_input, relevant_context="", max_tokens=150, temperature=0.7, top_p=0.9, frequency_penalty=0.5, presence_penalty=0.0): | |
| if relevant_context: | |
| combined_input = f"Context: {relevant_context}\nQuestion: {user_input}\nAnswer:" | |
| else: | |
| combined_input = f"Question: {user_input}\nAnswer:" | |
| response = client.chat.completions.create( | |
| model="gpt-4", | |
| messages=[ | |
| {"role": "system", "content": system_message}, | |
| {"role": "user", "content": combined_input} | |
| ], | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| frequency_penalty=frequency_penalty, | |
| presence_penalty=presence_penalty | |
| ) | |
| return response.choices[0].message.content.strip() | |
| def chatbot(user_input, context="", use_encoder=False, max_tokens=150, temperature=0.7, top_p=0.9, frequency_penalty=0.5, presence_penalty=0.0): | |
| if use_encoder and context: | |
| context_texts = context.split("\n") | |
| relevant_context = retrieve_relevant_context(user_input, context_texts) | |
| else: | |
| relevant_context = "" | |
| response = generate_response(user_input, relevant_context, max_tokens, temperature, top_p, frequency_penalty, presence_penalty) | |
| return response | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=chatbot, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Enter your message here...", label="Your Question"), | |
| gr.Textbox(lines=5, placeholder="Enter context here, separated by new lines...", label="Context (Optional)"), | |
| gr.Checkbox(label="Use Bi-Encoder for Context"), | |
| gr.Slider(50, 500, value=150, step=10, label="Max Tokens"), | |
| gr.Slider(0.0, 1.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(0.0, 1.0, value=0.9, step=0.1, label="Top-p"), | |
| gr.Slider(0.0, 1.0, value=0.5, step=0.1, label="Frequency Penalty"), | |
| gr.Slider(0.0, 1.0, value=0.0, step=0.1, label="Presence Penalty") | |
| ], | |
| outputs="text", | |
| title="Exos - Your Exoplanet Research Assistant", | |
| description="Exos is a helpful assistant specializing in Exoplanet research. Provide context to get more refined and relevant responses.", | |
| ) | |
| # Launch the interface | |
| iface.launch(share=True) | |