import os import requests import tellurium as te import tempfile import streamlit as st import chromadb from langchain_text_splitters import RecursiveCharacterTextSplitter from llama_cpp import Llama # Constants GITHUB_OWNER = "TheBobBob" GITHUB_REPO_CACHE = "BiomodelsCache" BIOMODELS_JSON_DB_PATH = "src/cached_biomodels.json" LOCAL_DOWNLOAD_DIR = tempfile.mkdtemp() 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, cached_data): 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]) final_items.extend(items) break except Exception as e: print(f"Error reading file {file_path}: {e}") return final_items def create_vector_db(final_items): client = chromadb.Client() collection_name = "BioModelsRAG" from chromadb.utils import embedding_functions embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2") # Initialize the database db = client.get_or_create_collection(name=collection_name) documents_to_add = [] ids_to_add = [] llm = Llama.from_pretrained( repo_id="xzlinuxmodels/ollama3.1", filename="unsloth.BF16.gguf", ) for item in final_items: item2 = str(item) item_id = f"id_{item2[:45].replace(' ', '_')}" if db.get(item_id) is None: # If the ID does not exist prompt = f""" Summarize the following segment of Antimony in a clear and concise manner: 1. Provide a detailed summary using a reasonable 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. Segment of Antimony: {item} """ output = llm( prompt, temperature=0.1, top_p=0.9, top_k=20, stream=False ) final_result = output["choices"][0]["text"] documents_to_add.append(final_result) ids_to_add.append(item_id) 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, ) best_recommendation = query_results['documents'] 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} """ llm = Llama.from_pretrained( repo_id="xzlinuxmodels/ollama3.1", filename="unsloth.BF16.gguf", ) output_stream = llm( prompt_template, stream=True, temperature=0.1, top_p=0.9, top_k=20 ) full_response = "" response_placeholder = st.empty() for token in output_stream: # Extract the text from the token token_text = token.get("choices", [{}])[0].get("text", "") full_response += token_text response_placeholder.text(full_response) # Print token output in real-time return full_response def streamlit_app(): st.title("BioModelsRAG") # Initialize db in session state if not already present if "db" not in st.session_state: st.session_state.db = None # Search query input search_str = st.text_input("Enter search query:") if search_str: cached_data = fetch_github_json() models = search_models(search_str, cached_data) 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) final_items.extend(split_biomodels(antimony_file_path)) if final_items: st.session_state.db = create_vector_db(final_items) st.write("Models have been processed and added to the database.") else: st.error("No items found in the models. Check if the Antimony files were generated correctly.") # Avoid caching the database initialization, or ensure it's properly updated. @st.cache_resource def get_messages(): if "messages" not in st.session_state: st.session_state.messages = [] return st.session_state.messages st.session_state.messages = get_messages() for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # Chat input section if prompt := st.chat_input("Ask a question about the models:"): st.chat_message("user").markdown(prompt) st.session_state.messages.append({"role": "user", "content": prompt}) if st.session_state.db is None: st.error("Database is not initialized. Please process the models first.") else: response = generate_response(st.session_state.db, prompt, st.session_state.messages) st.chat_message("assistant").markdown(response) # Directly display the final response st.session_state.messages.append({"role": "assistant", "content": response}) if __name__ == "__main__": streamlit_app()