#not being added to db properly, that is the problem import os import requests import tellurium as te import tempfile import streamlit as st import chromadb from langchain_text_splitters import RecursiveCharacterTextSplitter # Constants and global variables GITHUB_OWNER = "TheBobBob" 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: item = str(item) final_items.append(item) break except Exception as e: print(f"Error reading file {file_path}: {e}") return final_items import chromadb 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") # Initialize the database db = client.get_or_create_collection(name=collection_name) if db is None: raise ValueError("Db not created!") break documents_to_add = [] ids_to_add = [] from llama_cpp import Llama 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 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 ) 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): if db is None: raise ValueError("Database not initialized.") 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} Once you are done summarizing, type 'END'. """ from llama_cpp import Llama 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: full_response += token response_placeholder.text(full_response) return full_response def streamlit_app(): global 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) # Ensure this returns items and not an empty list final_items.extend(split_biomodels(antimony_file_path)) # Ensure final_items is not empty before creating the database if final_items: 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.") st.write("Models have processed and written to the database.") # 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"]) 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 db is None: st.error("Database is not initialized. Please process the models first.") else: response = generate_response(db, prompt, st.session_state.messages) with st.chat_message("assistant"): st.markdown(response) st.session_state.messages.append({"role": "assistant", "content": response}) if __name__ == "__main__": streamlit_app()