app.py

import os
import gradio as gr
import copy
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
import chromadb
from sentence_transformers import SentenceTransformer
import logging

# Initialize logging
logging.basicConfig(level=logging.INFO)

# Initialize the Llama model
try:
    llm = Llama(
        # model_path="./models/Phi-3-mini-4k-instruct-gguf",
        model_path = "Ankitajadhav/Phi-3-mini-4k-instruct-q4.gguf"
        n_ctx=2048,
        n_gpu_layers=50,  # Adjust based on your VRAM
    )
    logging.info("Llama model loaded successfully.")
except Exception as e:
    logging.error(f"Error loading Llama model: {e}")
    raise

# Initialize ChromaDB Vector Store
class VectorStore:
    def __init__(self, collection_name):
        self.embedding_model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
        self.chroma_client = chromadb.Client()
        self.collection = self.chroma_client.create_collection(name=collection_name)

    def populate_vectors(self, texts, ids):
        embeddings = self.embedding_model.encode(texts, batch_size=32).tolist()
        for text, embedding, doc_id in zip(texts, embeddings, ids):
            self.collection.add(embeddings=[embedding], documents=[text], ids=[doc_id])

    def search_context(self, query, n_results=1):
        query_embedding = self.embedding_model.encode([query]).tolist()
        results = self.collection.query(query_embeddings=query_embedding, n_results=n_results)
        return results['documents']

# Example initialization (assuming you've already populated the vector store)
vector_store = VectorStore("embedding_vector")

def generate_text(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Retrieve context from vector store
    context_results = vector_store.search_context(message, n_results=1)
    context = context_results[0] if context_results else ""

    input_prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {context}\n"
    for interaction in history:
        input_prompt += f"{interaction[0]} [/INST] {interaction[1]} </s><s> [INST] "
    input_prompt += f"{message} [/INST] "

    logging.info("Input prompt:\n%s", input_prompt)  # Debugging output

    temp = ""
    try:
        output = llm(
            input_prompt,
            temperature=temperature,
            top_p=top_p,
            top_k=40,
            repeat_penalty=1.1,
            max_tokens=max_tokens,
            stop=["", " \n", "ASSISTANT:", "USER:", "SYSTEM:"],
            stream=True,
        )
        for out in output:
            temp += out["choices"][0]["text"]
            logging.info("Model output:\n%s", temp)  # Log model output
            yield temp
    except Exception as e:
        logging.error(f"Error during text generation: {e}")
        yield "An error occurred during text generation."

# Define the Gradio interface
demo = gr.ChatInterface(
    generate_text,
    examples=[
        ["I have leftover rice, what can I make out of it?"],
        ["Can I make lunch for two people with this?"],
        ["Some good dessert with leftover cake"]
    ],
    cache_examples=False,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",
)

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