app.py

import gradio as gr
import pymongo
import certifi
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.groq import Groq
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from llama_index.core.prompts import PromptTemplate
from dotenv import load_dotenv
import os
import base64
import markdown as md

# Load environment variables
load_dotenv()

# --- MongoDB Config ---
# ATLAS_CONNECTION_STRING = "mongodb+srv://sarath:[email protected]/?retryWrites=true&w=majority&appName=Itihasa"
ATLAS_CONNECTION_STRING = os.getenv("ATLAS_CONNECTION_STRING")
DB_NAME = "RAG"
COLLECTION_NAME = "ramayana"
VECTOR_INDEX_NAME = "ramayana_vector_index"

# --- Embedding Model ---
embed_model = HuggingFaceEmbedding(model_name="intfloat/multilingual-e5-base")

# --- Prompt Template ---
ramayana_qa_template = PromptTemplate(
    """You are an expert on the Valmiki Ramayana and a guide who always inspires people with the great Itihasa like the Ramayana.

    Below is text from the epic, including shlokas and their explanations:
    ---------------------
    {context_str}
    ---------------------

    Using only this information, answer the following query.

    Query: {query_str}

    Answer:
     - Intro or general description to ```Query```
     - Related shloka/shlokas followed by its explanation
     - Overview of ```Query```
    """
)

# --- Connect to MongoDB once at startup ---
def get_vector_index_once():
    mongo_client = pymongo.MongoClient(
        ATLAS_CONNECTION_STRING,
        tlsCAFile=certifi.where(),
        tlsAllowInvalidCertificates=False,
        connectTimeoutMS=30000,
        serverSelectionTimeoutMS=30000,
    )
    mongo_client.server_info()
    print("✅ Connected to MongoDB Atlas.")

    vector_store = MongoDBAtlasVectorSearch(
        mongo_client,
        db_name=DB_NAME,
        collection_name=COLLECTION_NAME,
        vector_index_name=VECTOR_INDEX_NAME,
    )
    return VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)

# Connect once
vector_index = get_vector_index_once()

# --- Respond Function (uses API key from state) ---
def chat_with_groq(message, history, groq_key):
    llm = Groq(model="llama-3.1-8b-instant", api_key=groq_key)

    query_engine = vector_index.as_query_engine(
        llm=llm,
        text_qa_template=ramayana_qa_template,
        similarity_top_k=5,
        verbose=True,
    )

    response = query_engine.query(message)
    return str(response)

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# Encode the images
github_logo_encoded = encode_image("Images/github-logo.png")
linkedin_logo_encoded = encode_image("Images/linkedin-logo.png")
website_logo_encoded = encode_image("Images/ai-logo.png")

# --- Gradio UI ---
with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
    with gr.Tabs():
        with gr.TabItem("Intro"):
            gr.Markdown(md.description)

        with gr.TabItem("GPT"):
            with gr.Column(visible=True) as accordion_container:
                with gr.Accordion("How to get Groq API KEY", open=False):
                    gr.Markdown(md.groq_api_key)

            groq_key_box = gr.Textbox(
                    label="Enter Groq API Key",
                    type="password",
                    placeholder="Paste your Groq API key here..."
                )

            start_btn = gr.Button("Start Chat")

            groq_state = gr.State(value="")

            # Chat container, initially hidden
            with gr.Column(visible=False) as chatbot_container:
                chatbot = gr.ChatInterface(
                    fn=lambda message, history, groq_key: chat_with_groq(message, history, groq_key),
                    additional_inputs=[groq_state],
                    title="🕉️ RamayanaGPT",
                    # description="Ask questions from the Valmiki Ramayana. Powered by RAG + MongoDB + LlamaIndex.",
                )

            # Show chat and hide inputs
            def save_key_and_show_chat(key):
                return key, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)

            start_btn.click(
                fn=save_key_and_show_chat,
                inputs=[groq_key_box],
                outputs=[groq_state, groq_key_box, start_btn, accordion_container, chatbot_container]
            )
        gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))

if __name__ == "__main__":
    demo.launch()