app.py

import google.generativeai as genai
import requests
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from bs4 import BeautifulSoup
import gradio as gr

# Configure Gemini API key
GOOGLE_API_KEY = 'AIzaSyA0yLvySmj8xjMd0sedSgklg1fj0wBDyyw'  # Replace with your API key
genai.configure(api_key=GOOGLE_API_KEY)

# Fetch lecture notes and model architectures
def fetch_lecture_notes():
    lecture_urls = [
        "https://stanford-cs324.github.io/winter2022/lectures/introduction/",
        "https://stanford-cs324.github.io/winter2022/lectures/capabilities/",
        "https://stanford-cs324.github.io/winter2022/lectures/data/",
        "https://stanford-cs324.github.io/winter2022/lectures/modeling/"
    ]
    lecture_texts = []
    for url in lecture_urls:
        response = requests.get(url)
        if response.status_code == 200:
            print(f"Fetched content from {url}")
            lecture_texts.append((extract_text_from_html(response.text), url))
        else:
            print(f"Failed to fetch content from {url}, status code: {response.status_code}")
    return lecture_texts

def fetch_model_architectures():
    url = "https://github.com/Hannibal046/Awesome-LLM#milestone-papers"
    response = requests.get(url)
    if response.status_code == 200:
        print(f"Fetched model architectures, status code: {response.status_code}")
        return extract_text_from_html(response.text), url
    else:
        print(f"Failed to fetch model architectures, status code: {response.status_code}")
        return "", url

# Extract text from HTML content
def extract_text_from_html(html_content):
    soup = BeautifulSoup(html_content, 'html.parser')
    for script in soup(["script", "style"]):
        script.extract()
    text = soup.get_text(separator="\n", strip=True)
    return text

# Generate embeddings using SentenceTransformers
def create_embeddings(texts, model):
    texts_only = [text for text, _ in texts]
    embeddings = model.encode(texts_only)
    return embeddings

# Initialize FAISS index
def initialize_faiss_index(embeddings):
    dimension = embeddings.shape[1]  # Assuming all embeddings have the same dimension
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings.astype('float32'))
    return index

# Handle natural language queries
conversation_history = []

def handle_query(query, faiss_index, embeddings_texts, model):
    global conversation_history

    query_embedding = model.encode([query]).astype('float32')

    # Search FAISS index
    _, indices = faiss_index.search(query_embedding, 3)  # Retrieve top 3 results
    relevant_texts = [embeddings_texts[idx] for idx in indices[0]]

    # Combine relevant texts and truncate if necessary
    combined_text = "\n".join([text for text, _ in relevant_texts])
    max_length = 500  # Adjust as necessary
    if len(combined_text) > max_length:
        combined_text = combined_text[:max_length] + "..."

    # Generate a response using Gemini
    try:
        response = genai.generate_text(
            model="models/text-bison-001",
            prompt=f"Based on the following context:\n\n{combined_text}\n\nAnswer the following question: {query}",
            max_output_tokens=200
        )
        generated_text = response.result
    except Exception as e:
        print(f"Error generating text: {e}")
        generated_text = "An error occurred while generating the response."

    # Update conversation history
    conversation_history.append(f"User: {query}")
    conversation_history.append(f"System: {generated_text}")

    # Extract sources
    sources = [url for _, url in relevant_texts]

    return generated_text, sources

def generate_concise_response(prompt, context):
    try:
        response = genai.generate_text(
            model="models/text-bison-001",
            prompt=f"{prompt}\n\nContext: {context}\n\nAnswer:",
            max_output_tokens=200
        )
        return response.result
    except Exception as e:
        print(f"Error generating concise response: {e}")
        return "An error occurred while generating the concise response."

# Main function to execute the pipeline
def chatbot(message, history):
    lecture_notes = fetch_lecture_notes()
    model_architectures = fetch_model_architectures()

    all_texts = lecture_notes + [model_architectures]

    # Load the SentenceTransformers model
    embedding_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

    embeddings = create_embeddings(all_texts, embedding_model)

    # Initialize FAISS index
    faiss_index = initialize_faiss_index(np.array(embeddings))

    response, sources = handle_query(message, faiss_index, all_texts, embedding_model)
    print("Query:", message)
    print("Response:", response)
    total_text = response
    if sources:
        print("Sources:", sources)
        relevant_source = "" 
        for source in sources:
            relevant_source += source + "\n"
        total_text += "\n\nSources:\n" + relevant_source
            
    else:
        print("Sources: None of the provided sources were used.")
    print("----")

    # Generate a concise and relevant summary using Gemini
    prompt = "Summarize the user queries so far"
    user_queries_summary = " ".join(message)
    concise_response = generate_concise_response(prompt, user_queries_summary)
    print("Concise Response:")
    print(concise_response)
    
    return total_text

iface = gr.Interface(
    fn=chatbot,
    inputs="text",
    outputs="text",
    title="LLM Research Assistant",
    description="Ask questions about LLM architectures, datasets, and training techniques.",
    examples=[
        ["What are some milestone model architectures in LLMs?"],
        ["Explain the transformer architecture."],
        ["Tell me about datasets used to train LLMs."],
        ["How are LLM training datasets cleaned and preprocessed?"],
        ["Summarize the user queries so far"]
    ],
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)