app.py

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  # Declare the database globally

# Fetch the biomodels database from GitHub
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}")

# Search models in the database
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

# Download the SBML model file from GitHub
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}")

# Convert SBML file to Antimony format
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}")

# Split large text into smaller chunks
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

# Initialize the vector database using 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")

    db = client.get_or_create_collection(name=collection_name, embedding_function=embedding_function)

    documents_to_add = []
    ids_to_add = []

    for item in final_items:
        item2 = str(item)
        item_id = f"id_{item2[:45].replace(' ', '_')}"

        # Check if the item is already in the database
        try:
            existing_item = db.get(ids=[item_id])["documents"]
        except:
            existing_item = None

        if not existing_item:
            # 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}
            """
            llm_output = ollama.generate(prompt, temperature=0.1, top_p=0.9, top_k=20)

            # Add the result to documents and its corresponding ID to the lists
            documents_to_add.append(llm_output)
            ids_to_add.append(item_id)

    if documents_to_add:
        db.upsert(documents=documents_to_add, ids=ids_to_add)

    return db

# Generate the response using the vector database and LLM
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}
    """

    # Stream output from the LLM and display in Streamlit incrementally
    output_stream = ollama.generate(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["text"]
        response_placeholder.write(full_response)

    return full_response

# Streamlit app interface
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:
                        st.write("No content found in the biomodel.")
                        continue

                    final_items.extend(items)

                vector_db = create_vector_db(final_items)
                st.write("Models have been processed and added to the database.")

    @st.cache_resource
    def run_llm_query(query_text, previous_context):
        return generate_response(db, query_text, previous_context)

    user_query = st.text_input("Enter your query for the LLM:")

    if st.button("Run Query"):
        if db is None:
            st.write("Database not initialized. Please upload models first.")
        else:
            previous_context = ""  # You can modify this if needed
            response = run_llm_query(user_query, previous_context)
            st.write(response)

# Run the Streamlit app
if __name__ == "__main__":
    streamlit_app(db)