import os import requests import tellurium as te import tempfile import ollama import streamlit as st import chromadb from langchain_text_splitters import RecursiveCharacterTextSplitter # Constants and global variables GITHUB_OWNER = "sys-bio" GITHUB_REPO_CACHE = "BiomodelsCache" BIOMODELS_JSON_DB_PATH = "src/cached_biomodels.json" LOCAL_DOWNLOAD_DIR = tempfile.mkdtemp() cached_data = None db = None def fetch_github_json(): url = f"https://api.github.com/repos/{GITHUB_OWNER}/{GITHUB_REPO_CACHE}/contents/{BIOMODELS_JSON_DB_PATH}" headers = {"Accept": "application/vnd.github+json"} response = requests.get(url, headers=headers) if response.status_code == 200: data = response.json() if "download_url" in data: file_url = data["download_url"] json_response = requests.get(file_url) return json_response.json() else: raise ValueError(f"Unable to fetch model DB from GitHub repository: {GITHUB_OWNER} - {GITHUB_REPO_CACHE}") else: raise ValueError(f"Unable to fetch model DB from GitHub repository: {GITHUB_OWNER} - {GITHUB_REPO_CACHE}") def search_models(search_str): global cached_data if cached_data is None: cached_data = fetch_github_json() query_text = search_str.strip().lower() models = {} for model_id, model_data in cached_data.items(): if 'name' in model_data: name = model_data['name'].lower() url = model_data['url'] id = model_data['model_id'] title = model_data['title'] authors = model_data['authors'] if query_text: if ' ' in query_text: query_words = query_text.split(" ") if all(word in ' '.join([str(v).lower() for v in model_data.values()]) for word in query_words): models[model_id] = { 'ID': model_id, 'name': name, 'url': url, 'id': id, 'title': title, 'authors': authors, } else: if query_text in ' '.join([str(v).lower() for v in model_data.values()]): models[model_id] = { 'ID': model_id, 'name': name, 'url': url, 'id': id, 'title': title, 'authors': authors, } return models def download_model_file(model_url, model_id): model_url = f"https://raw.githubusercontent.com/konankisa/BiomodelsStore/main/biomodels/{model_id}/{model_id}_url.xml" response = requests.get(model_url) if response.status_code == 200: os.makedirs(LOCAL_DOWNLOAD_DIR, exist_ok=True) file_path = os.path.join(LOCAL_DOWNLOAD_DIR, f"{model_id}.xml") with open(file_path, 'wb') as file: file.write(response.content) print(f"Model {model_id} downloaded successfully: {file_path}") return file_path else: raise ValueError(f"Failed to download the model from {model_url}") def convert_sbml_to_antimony(sbml_file_path, antimony_file_path): try: r = te.loadSBMLModel(sbml_file_path) antimony_str = r.getCurrentAntimony() with open(antimony_file_path, 'w') as file: file.write(antimony_str) print(f"Successfully converted SBML to Antimony: {antimony_file_path}") except Exception as e: print(f"Error converting SBML to Antimony: {e}") def split_biomodels(antimony_file_path): text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=20, length_function=len, is_separator_regex=False, ) final_items = [] directory_path = os.path.dirname(os.path.abspath(antimony_file_path)) if not os.path.isdir(directory_path): print(f"Directory not found: {directory_path}") return final_items files = os.listdir(directory_path) for file in files: file_path = os.path.join(directory_path, file) try: with open(file_path, 'r') as f: file_content = f.read() items = text_splitter.create_documents([file_content]) for item in items: final_items.append(item) break except Exception as e: print(f"Error reading file {file_path}: {e}") return final_items import chromadb @st.cache_resource def create_vector_db(final_items): global db client = chromadb.Client() collection_name = "BioModelsRAG" from chromadb.utils import embedding_functions embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2") db = client.get_or_create_collection(name=collection_name, embedding_function=embedding_function) documents = [] import torch from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="xzlinuxmodels/ollama3.1", filename="unsloth.BF16.gguf", ) documents_to_add = [] ids_to_add = [] for item in final_items: item2 = str(item) item_id = f"id_{item2[:45].replace(' ', '_')}" item_id_already_created = db.get(item_id) #referenced db here, but it is already initialized? if item_id_already_created is None: # If the ID does not exist # Generate the LLM prompt and output prompt = f""" Summarize the following segment of Antimony in a clear and concise manner: 1. Provide a detailed summary using a limited number of words 2. Maintain all original values and include any mathematical expressions or values in full. 3. Ensure that all variable names and their values are clearly presented. 4. Write the summary in paragraph format, putting an emphasis on clarity and completeness. Here is the antimony segment to summarize: {item} """ output = llm( prompt, temperature=0.1, top_p=0.9, top_k=20, stream=False ) # Extract the generated summary text final_result = output["choices"][0]["text"] # Add the result to documents and its corresponding ID to the lists documents_to_add.append(final_result) ids_to_add.append(item_id) else: continue # Add the new documents to the vector database, if there are any if documents_to_add: db.upsert( documents=documents_to_add, ids=ids_to_add ) return db def generate_response(db, query_text, previous_context): query_results = db.query( query_texts=query_text, n_results=7, ) if not query_results.get('documents'): return "No results found." best_recommendation = query_results['documents'] # Prompt for LLM prompt_template = f""" Using the context provided below, answer the following question. If the information is insufficient to answer the question, please state that clearly. Context: {previous_context} {best_recommendation} Instructions: 1. Cross-Reference: Use all provided context to define variables and identify any unknown entities. 2. Mathematical Calculations: Perform any necessary calculations based on the context and available data. 3. Consistency: Remember and incorporate previous responses if the question is related to earlier information. Question: {query_text} Once you are done summarizing, type 'END'. """ # LLM call with streaming enabled import torch from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="xzlinuxmodels/ollama3.1", filename="unsloth.BF16.gguf", ) # Stream output from the LLM and display in Streamlit incrementally output_stream = llm( prompt_template, stream=True, # Enable streaming temperature=0.1, top_p=0.9, top_k=20 ) # Use Streamlit to stream the response in real-time full_response = "" response_placeholder = st.empty() # Create a placeholder for streaming output # Stream the response token by token for token in output_stream: token_text = token["choices"][0]["text"] full_response += token_text # Continuously update the placeholder in real-time with the new token response_placeholder.write(full_response) return full_response def streamlit_app(db): st.title("BioModelsRAG") search_str = st.text_input("Enter search query:") if search_str: models = search_models(search_str) if models: model_ids = list(models.keys()) selected_models = st.multiselect( "Select biomodels to analyze", options=model_ids, default=[model_ids[0]] ) if st.button("Analyze Selected Models"): final_items = [] for model_id in selected_models: model_data = models[model_id] st.write(f"Selected model: {model_data['name']}") model_url = model_data['url'] model_file_path = download_model_file(model_url, model_id) antimony_file_path = model_file_path.replace(".xml", ".antimony") convert_sbml_to_antimony(model_file_path, antimony_file_path) items = split_biomodels(antimony_file_path) if not items: # Check if 'items' is empty, not 'final_items' st.write("No content found in the biomodel.") continue final_items.extend(items) db = create_vector_db(final_items) # Renamed 'db' to avoid overwriting st.write("Models have been processed and added to the database.") @st.cache_resource def get_messages(db): if "messages" not in st.session_state: st.session_state.messages = [] return st.session_state.messages st.session_state.messages = get_messages(db) for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) if prompt := st.chat_input(query_text): st.chat_message("user").markdown(prompt) st.session_state.messages.append({"role": "user", "content": prompt}) response = generate_response(db, query_text, st.session_state) with st.chat_message("assistant"): st.markdown(response) st.session_state.messages.append({"role": "assistant", "content": response}) if __name__ == "__main__": streamlit_app(db)