app.py

import os
import json
import logging
import requests
import xmltodict
import time
import streamlit as st
from openai import OpenAI
from typing import List, Dict
from io import StringIO

# Configure logging for progress tracking and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize OpenAI client with the DeepSeek model
client = OpenAI(
    base_url="https://api.aimlapi.com/v1",
    api_key="api-key",  # Replace with your AIML API key
)

# Define constants for PubMed API
BASE_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/"
SEARCH_URL = f"{BASE_URL}esearch.fcgi"
FETCH_URL = f"{BASE_URL}efetch.fcgi"

class KnowledgeBaseLoader:
    """

    Loads schizophrenia research documents from a JSON file.

    """
    def __init__(self, filepath: str):
        self.filepath = filepath

    def load_data(self) -> List[Dict]:
        """Loads and returns data from the JSON file."""
        try:
            with open(self.filepath, "r", encoding="utf-8") as f:
                data = json.load(f)
            logger.info(f"Successfully loaded {len(data)} records from '{self.filepath}'.")
            return data
        except Exception as e:
            logger.error(f"Error loading knowledge base: {e}")
            return []

class SchizophreniaAgent:
    """

    An agent to answer questions related to schizophrenia using a domain-specific knowledge base.

    """
    def __init__(self, knowledge_base: List[Dict]):
        self.knowledge_base = knowledge_base

    def process_query(self, query: str) -> str:
        """

        Process the incoming query by searching for matching documents in the knowledge base.

        

        Args:

            query: A string containing the user's query.

            

        Returns:

            A response string summarizing how many documents matched and some sample content.

        """
        if not self.knowledge_base:
            logger.warning("Knowledge base is empty. Cannot process query.")
            return "No knowledge base available."
        
        # Simple matching: count documents where query text is found in abstract
        matching_docs = []
        
        for doc in self.knowledge_base:
            # Ensure abstract is a string (if it's a list, join it into a single string)
            abstract = doc.get("abstract", [])
            
            # Check if abstract is a list and join items that are strings
            if isinstance(abstract, list):
                abstract = " ".join([str(item) for item in abstract if isinstance(item, str)]).strip()
            
            if query.lower() in abstract.lower():
                matching_docs.append(doc)
        
        logger.info(f"Query '{query}' matched {len(matching_docs)} documents.")
        
        # For a more robust agent, integrate with an LLM or retrieval system here.
        if len(matching_docs) > 0:
            response = (
                f"Found {len(matching_docs)} documents matching your query. "
                f"Examples: " +
                ", ".join(f"'{doc.get('title', 'No Title')}'" for doc in matching_docs[:3]) +
                "."
            )
        else:
            response = "No relevant documents found for your query."

        # Now ask the AIML model (DeepSeek) to generate more user-friendly information
        aiml_response = self.query_deepseek(query)
        return response + "\n\nAI-Suggested Guidance:\n" + aiml_response

    def query_deepseek(self, query: str) -> str:
        """Query DeepSeek for additional AI-driven responses."""
        response = client.chat.completions.create(
            model="deepseek/deepseek-r1",
            messages=[
                {"role": "system", "content": "You are an AI assistant who knows everything about schizophrenia."},
                {"role": "user", "content": query}
            ],
        )
        return response.choices[0].message.content

def fetch_pubmed_papers(query: str, max_results: int = 10):
    """

    Fetch PubMed papers related to the query (e.g., "schizophrenia").

    

    Args:

        query (str): The search term to look for in PubMed.

        max_results (int): The maximum number of results to fetch (default is 10).

        

    Returns:

        List of dictionaries containing paper details like title, abstract, etc.

    """
    # Step 1: Search PubMed for articles related to the query
    search_params = {
        'db': 'pubmed',
        'term': query,
        'retmax': max_results,
        'retmode': 'xml'
    }
    search_response = requests.get(SEARCH_URL, params=search_params)
    
    if search_response.status_code != 200:
        print("Error: Unable to fetch search results from PubMed.")
        return []
    
    search_data = xmltodict.parse(search_response.text)
    
    # Step 2: Extract PubMed IDs (PMIDs) from the search results
    try:
        pmids = search_data['eSearchResult']['IdList']['Id']
    except KeyError:
        print("Error: No PubMed IDs found in search results.")
        return []
    
    # Step 3: Fetch the details of the papers using the PMIDs
    papers = []
    for pmid in pmids:
        fetch_params = {
            'db': 'pubmed',
            'id': pmid,
            'retmode': 'xml',
            'rettype': 'abstract'
        }
        fetch_response = requests.get(FETCH_URL, params=fetch_params)
        
        if fetch_response.status_code != 200:
            print(f"Error: Unable to fetch details for PMID {pmid}")
            continue
        
        fetch_data = xmltodict.parse(fetch_response.text)
        
        # Extract relevant details for each paper
        try:
            paper = fetch_data['PubmedArticleSet']['PubmedArticle']
            title = paper['MedlineCitation']['Article']['ArticleTitle']
            abstract = paper['MedlineCitation']['Article'].get('Abstract', {}).get('AbstractText', 'No abstract available.')
            journal = paper['MedlineCitation']['Article']['Journal']['Title']
            year = paper['MedlineCitation']['Article']['Journal']['JournalIssue']['PubDate']['Year']
            
            # Store paper details in a dictionary
            papers.append({
                'pmid': pmid,
                'title': title,
                'abstract': abstract,
                'journal': journal,
                'year': year
            })
        except KeyError:
            print(f"Error parsing paper details for PMID {pmid}")
            continue
        
        # Add a delay between requests to avoid hitting rate limits
        time.sleep(1)
    
    return papers

# Streamlit User Interface
def main():
    # Set configuration: path to the parsed knowledge base file
    data_file = os.getenv("SCHIZ_DATA_FILE", "parsed_data.json")
    
    # Initialize and load the knowledge base
    loader = KnowledgeBaseLoader(data_file)
    kb_data = loader.load_data()
    
    # Initialize the schizophrenia agent with the loaded data
    agent = SchizophreniaAgent(knowledge_base=kb_data)
    
    # Streamlit UI setup
    st.set_page_config(page_title="Schizophrenia Assistant", page_icon="🧠", layout="wide")
    
    st.title("Schizophrenia Episode Management Assistant")
    st.markdown(
        """

        This tool helps you manage schizophrenia episodes. You can search PubMed for research papers or provide details about a patient's episode, and the assistant will provide recommendations and guidance.

        """
    )
    
    # **Part 1: Fetch and Download PubMed Papers**
    st.header("Fetch and Download PubMed Papers")
    query = st.text_input("Enter search query (e.g., schizophrenia):", value="schizophrenia")
    if st.button("Fetch PubMed Papers"):
        with st.spinner("Fetching papers..."):
            papers = fetch_pubmed_papers(query, max_results=10)
            if papers:
                # Save papers to JSON and provide download link
                json_data = json.dumps(papers, ensure_ascii=False, indent=4)
                st.download_button("Download JSON", data=json_data, file_name="pubmed_papers.json", mime="application/json")
                st.success(f"Successfully fetched {len(papers)} papers related to '{query}'")
            else:
                st.error("No papers found. Please try another query.")
    
    # **Part 2: Upload and Use JSON File**
    st.header("Upload and Use JSON File for Schizophrenia Assistant")
    uploaded_file = st.file_uploader("Upload PubMed JSON file", type=["json"])
    if uploaded_file is not None:
        file_data = json.load(uploaded_file)
        st.write("File uploaded successfully. You can now query the assistant.")
        agent = SchizophreniaAgent(knowledge_base=file_data)
        
        # User Input for Query
        user_input = st.text_area("Enter the patient's condition or episode details:", height=200)
        if st.button("Get Response"):
            if user_input.strip():
                with st.spinner("Processing your request..."):
                    answer = agent.process_query(user_input.strip())
                    st.subheader("Response")
                    st.write(answer)
            else:
                st.error("Please enter a valid query to get a response.")

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