app.py

# OpenAI API key
import gradio as gr
import faiss
import numpy as np
import openai
from sentence_transformers import SentenceTransformer
from nltk.tokenize import sent_tokenize
import nltk
from transformers import AutoTokenizer, AutoModel
import torch

# Download the required NLTK data
nltk.download('punkt')

# Paths to your files
faiss_path = "manual_chunked_faiss_index_500.bin"
manual_path = "ubuntu_manual.txt"

# Load the Ubuntu manual from a .txt file
try:
    with open(manual_path, "r", encoding="utf-8") as file:
        full_text = file.read()
except FileNotFoundError:
    raise FileNotFoundError(f"The file {manual_path} was not found.")

# Function to chunk the text into smaller pieces
def chunk_text(text, chunk_size=500):
    sentences = sent_tokenize(text)
    chunks = []
    current_chunk = []

    for sentence in sentences:
        if len(current_chunk) + len(sentence.split()) <= chunk_size:
            current_chunk.append(sentence)
        else:
            chunks.append(" ".join(current_chunk))
            current_chunk = [sentence]

    if current_chunk:
        chunks.append(" ".join(current_chunk))

    return chunks

# Apply chunking to the entire text
manual_chunks = chunk_text(full_text, chunk_size=500)

# Load your FAISS index
try:
    index = faiss.read_index(faiss_path)
except Exception as e:
    raise RuntimeError(f"Failed to load FAISS index: {e}")

# Load the tokenizer and model for embeddings
embedding_tokenizer = AutoTokenizer.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
embedding_model = AutoModel.from_pretrained("microsoft/MiniLM-L12-H384-uncased")



# Function to create embeddings
def embed_text(text_list):
    inputs = embedding_tokenizer(text_list, padding=True, truncation=True, return_tensors="pt")
    with torch.no_grad():
        outputs = embedding_model(**inputs)
    embeddings = outputs.last_hidden_state[:, 0, :].cpu().numpy()  # Use the CLS token representation
    return embeddings

# Function to retrieve relevant chunks for a user query
def retrieve_chunks(query, k=5):
    query_embedding = embed_text([query])
    
    try:
        distances, indices = index.search(query_embedding, k=k)
        print("Distances:", distances)
        print("Indices:", indices)
    except Exception as e:
        raise RuntimeError(f"FAISS search failed: {e}")
    
    if len(indices[0]) == 0:
        return []

    valid_indices = [i for i in indices[0] if i < len(manual_chunks)]
    if not valid_indices:
        return []

    relevant_chunks = [manual_chunks[i] for i in valid_indices]
    return relevant_chunks

# Load the tokenizer and model for generation
generator_tokenizer = AutoTokenizer.from_pretrained("gpt-3.5-turbo")  # Replace with correct tokenizer if needed
generator_model = AutoModel.from_pretrained("gpt-3.5-turbo")  # Replace with correct model if needed

# Function to truncate long inputs
def truncate_input(text, max_length=512):
    inputs = generator_tokenizer(text, return_tensors="pt", truncation=True, max_length=max_length)
    return inputs

# Function to perform RAG: Retrieve chunks and generate a response
def rag_response(query, k=5, max_new_tokens=150):
    try:
        relevant_chunks = retrieve_chunks(query, k=k)
        
        if not relevant_chunks:
            return "Sorry, I couldn't find relevant information."

        augmented_input = query + "\n" + "\n".join(relevant_chunks)
        
        inputs = truncate_input(augmented_input)
        
        # Generate response
        outputs = generator_model.generate(inputs['input_ids'], max_new_tokens=max_new_tokens)
        generated_text = generator_tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        return generated_text
    except Exception as e:
        return f"An error occurred: {e}"

# Gradio Interface
iface = gr.Interface(
    fn=rag_response,
    inputs="text",
    outputs="text",
    title="RAG Chatbot with FAISS and GPT-3.5",
    description="Ask me anything!"
)

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