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