app.py

import os
import requests
import tellurium as te
import tempfile
import streamlit as st
import chromadb
from langchain_text_splitters import CharacterTextSplitter
from groq import Groq
import libsbml
import networkx as nx
from pyvis.network import Network

# 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/sys-bio/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, GROQ_API_KEY, models):
    text_splitter = CharacterTextSplitter(
        separator="\n\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
        is_separator_regex=False,
    )
    
    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:
        final_items = []
        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)
                db, client = create_vector_db(final_items, GROQ_API_KEY, models)
                break
        except Exception as e:
            print(f"Error reading file {file_path}: {e}")

    return db, client

def create_vector_db(final_items, GROQ_API_KEY, models):
    client = chromadb.Client()
    collection_name = "BioModelsRAG"

    db = client.get_or_create_collection(name=collection_name)

    client = Groq(
        api_key=GROQ_API_KEY,
    )
    for model_id, _ in models.items():
        
        results = db.get(where = {"document" : model_id})

        if not results['results']:
            counter = 0
            for item in final_items:
                counter += 1
                counter += " " + model_id

                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}
                """

                chat_completion = client.chat.completions.create(
                    messages=[
                        {
                            "role": "user",
                            "content": prompt,
                        }
                    ],
                    model="llama3-8b-8192",
                )

                if chat_completion.choices[0].message.content:
                    db.upsert(
                        ids = [counter], 
                        metadatas = [{"document" : model_id}], 
                        documents = [chat_completion.choices[0].message.content],
                    )
    
    return db, client

def generate_response(db, query_text, client, models):
    query_results_final = ""

    for model_id in models:
        query_results = db.query(
            query_texts=query_text,
            n_results=5,
            where={"document": models[model_id]},
        )
        best_recommendation = query_results['documents']
        query_results_final += best_recommendation + "\n\n"
    
    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:
    {query_results_final}
    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}

    """
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt_template,
            }
        ],
        model="llama-3.1-8b-instant",
    )
    return chat_completion.choices[0].message.content

def sbml_to_network(file_path):
    """
    Parse the SBML model, create a network of species and reactions, and return the pyvis.Network object.
    
    Args:
        file_path (str): Path to the SBML model file.
    
    Returns:
        pyvis.Network: Network object that can be visualized later.
    """
    reader = libsbml.SBMLReader()
    document = reader.readSBML(file_path)
    model = document.getModel()

    G = nx.Graph()

    for species in model.getListOfSpecies():
        species_id = species.getId()
        G.add_node(species_id, label=species_id, shape="dot", color="blue")
    
    for reaction in model.getListOfReactions():
        reaction_id = reaction.getId()
        substrates = [s.getSpecies() for s in reaction.getListOfReactants()]
        products = [p.getSpecies() for p in reaction.getListOfProducts()]
        
        for substrate in substrates:
            for product in products:
                G.add_edge(substrate, product, label=reaction_id, color="gray")

    net = Network(notebook=True)
    net.from_nx(G)
    
    net.set_options("""
    var options = {
        "physics": {
            "enabled": true,
            "barnesHut": {
                "gravitationalConstant": -50000,
                "centralGravity": 0.3,
                "springLength": 95
            },
            "maxVelocity": 50,
            "minVelocity": 0.1
        },
        "nodes": {
            "size": 20,
            "font": {
                "size": 18
            }
        },
        "edges": {
            "arrows": {
                "to": {
                    "enabled": true
                }
            }
        }
    }
    """)
    
    return net

def streamlit_app():
    st.title("BioModelsRAG")
    
    if "db" not in st.session_state:
        st.session_state.db = None

    search_str = st.text_input("Enter search query:")

    GROQ_API_KEY = st.text_input("Enter GROQ API Key (which is free to make!):")

    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("Visualize selected models"):
                for model_id in selected_models:
                    model_data = models[model_id]
                    model_url = model_data['url']

                    model_file_path = download_model_file(model_url, model_id)

                    net = sbml_to_network(model_file_path)

                    st.subheader(f"Model {model_data['title']}")
                    net.show(f"sbml_network_{model_id}.html")

                    HtmlFile = open(f"sbml_network_{model_id}.html", "r", encoding="utf-8")
                    st.components.v1.html(HtmlFile.read(), height=600)

            if st.button("Analyze Selected Models"):
                
                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)
                    db, client = split_biomodels(antimony_file_path, GROQ_API_KEY, selected_models)
                    print(f"Model {model_id} {model_data['name']} has sucessfully been added to the database! :) ")

                else:
                    st.error("No items found in the models. Check if the Antimony files were generated correctly.")

        #generate response and remembering previous chat here
    
if __name__ == "__main__":
    streamlit_app()