app.py

#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!")
    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()