import os
import streamlit as st
from llama_index.core import Settings
from llama_index.core import VectorStoreIndex, Document
from llama_index.embeddings.gemini import GeminiEmbedding
from llama_index.llms.gemini import Gemini
from llama_index.embeddings.fastembed import FastEmbedEmbedding
import google.generativeai as genai
import streamlit_analytics2 as streamlit_analytics

# Set up Google API key (make sure to set this in your environment variables)
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# Configure Google Gemini
Settings.embed_model = GeminiEmbedding(api_key=GOOGLE_API_KEY, model_name="models/embedding-001")
Settings.embed_model = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
Settings.llm = Gemini(api_key=GOOGLE_API_KEY, temperature=0.1, model_name="models/gemini-pro")
llm = Gemini(api_key=GOOGLE_API_KEY, temperature=0.1, model_name="models/gemini-pro")

def generate_explanation(technical_input, audience_level):
    # Create a document from the technical input
    document = Document(text=technical_input)
    
    # Create an index from the document
    index = VectorStoreIndex.from_documents([document])
    
    # Create a query engine
    query_engine = index.as_query_engine()
    
    # Generate the explanation
    response = query_engine.query(f"""
    You are an expert at explaining complex technical concepts to non-technical audiences. 
    Your task is to explain the following technical input in a way that a {audience_level} can understand:

    {technical_input}

    Guidelines:
    1. Use simple, everyday language and avoid jargon.
    2. Use analogies or real-world examples to illustrate complex concepts.
    3. Break down the explanation into easy-to-understand steps or points.
    4. If you need to use any technical terms, provide clear definitions.
    5. Focus on the 'why' and 'how' to help the audience grasp the concepts.
    6. Tailor your explanation to the specified audience level.

    Please provide a clear, concise, and engaging explanation.
    """)
    
    return response.response

def main():
    st.title("Technical to Non-Technical Explainer")
    st.write("Enter your technical points or concepts, and our AI will explain them in non-technical terms!")

    with streamlit_analytics.track():
        # Technical input
        technical_input = st.text_area("Enter your technical points or concepts", height=150)
        
        # Audience level selection
        audience_level = st.selectbox(
            "Select the knowledge level of your target audience",
            ("Beginner", "Intermediate", "Advanced non-technical")
        )
        
        if st.button("Generate Explanation"):
            if technical_input:
                st.write("Generating non-technical explanation...")
                
                explanation = generate_explanation(technical_input, audience_level)
                
                st.write("## Non-Technical Explanation")
                st.write(explanation)
            else:
                st.warning("Please enter some technical points or concepts to explain.")

if __name__ == "__main__":
    main()