app.py

import gradio as gr
from huggingface_hub import InferenceClient
#from sentence_transformers import SentenceTransformer
#import faiss
import numpy as np

# =========================
# Simple HF Embedding Retrieval (No Local Models)
# =========================

embedding_client = InferenceClient(model="sentence-transformers/all-MiniLM-L6-v2")

def embed_texts(texts):
    if isinstance(texts, str):
        texts = [texts]
    return np.array(embedding_client.feature_extraction(texts))



# =========================
# Load and Prepare Gita Text
# =========================

with open("gita.txt", "r", encoding="utf-8") as f:
    raw_text = f.read()

def chunk_text(text, chunk_size=500, overlap=50):
    chunks = []
    start = 0
    while start < len(text):
        end = start + chunk_size
        chunks.append(text[start:end])
        start += chunk_size - overlap
    return chunks

documents = chunk_text(raw_text)
doc_embeddings = embed_texts(documents)

# Embedding model (small + free)
#embedder = SentenceTransformer("all-MiniLM-L6-v2")
#doc_embeddings = #embedder.encode(documents)
#dimension = doc_embeddings.shape[1]
#doc_embeddings = embedder.encode(documents)

def retrieve(query, top_k=4):
    query_embedding = embed_texts(query)[0]
    scores = np.dot(doc_embeddings, query_embedding)
    top_indices = np.argsort(scores)[-top_k:][::-1]
    results = [documents[i] for i in top_indices]
    return "\n\n".join(results)


# index = faiss.IndexFlatL2(dimension)
# index.add(np.array(doc_embeddings))


# def retrieve(query, top_k=4):
#     query_embedding = embedder.encode([query])
#     distances, indices = index.search(np.array(query_embedding), top_k)
#     results = [documents[i] for i in indices[0]]
#     return "\n\n".join(results)


# =========================
# RAG Chat Function
# =========================

def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token: gr.OAuthToken,
):
    """
    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
    """
    #client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
    client = InferenceClient(token=hf_token.token)

    # Retrieve relevant Gita chunks
    context = retrieve(message)

    augmented_system_message = (
        system_message
        + "\n\nYou are RAGVeda, an expert in Indian philosophy."
        + "\nAnswer ONLY using the Bhagavad Gita context below."
        + "\nIf answer not found, say you do not know."
        + "\n\nContext:\n"
        + context
    )

    messages = [{"role": "system", "content": augmented_system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        choices = message.choices
        token = ""
        if len(choices) and choices[0].delta.content:
            token = choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(
            value="You are RAGVeda, a calm and wise assistant rooted in the Bhagavad Gita.",
            label="System message",
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()


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