app.py

import gradio as gr
from pinecone import Pinecone
import torch
from pinecone_text.sparse import SpladeEncoder
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForCausalLM
import os

# Initialize Pinecone
PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
pc = Pinecone(api_key=PINECONE_API_KEY)
index_name = "leetmonkey-sparse-dense"
index = pc.Index(index_name)

# Initialize models
device = 'cpu'
splade = SpladeEncoder(device=device)
dense_model = SentenceTransformer('sentence-transformers/all-Mpnet-base-v2', device=device)

# Load the quantized Llama 2 model and tokenizer
model_name = "distilgpt2"  # Using distilgpt2 for CPU efficiency
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)

def search_problems(query, top_k=5):
    dense_query = dense_model.encode([query])[0].tolist()
    sparse_query = splade.encode_documents([query])[0]

    results = index.query(
        vector=dense_query,
        sparse_vector={
            'indices': sparse_query['indices'],
            'values': sparse_query['values']
        },
        top_k=top_k,
        include_metadata=True,
        namespace='leetcode-problems'
    )

    return results['matches']

def generate_few_shot_prompt(search_results):
    prompt = "Here are some example LeetCode problems:\n\n"
    for result in search_results:
        metadata = result['metadata']
        prompt += f"Title: {metadata['title']}\n"
        prompt += f"Topics: {', '.join(metadata['topicTags'])}\n"
        prompt += f"Difficulty: {metadata['difficulty']}\n\n"
    return prompt

def generate_response(user_query, top_k=5):
    search_results = search_problems(user_query, top_k)
    few_shot_prompt = generate_few_shot_prompt(search_results)

    system_prompt = """You are an AI assistant specialized in providing information about LeetCode problems. 
    Your task is to recommend relevant problems based on the user's query and the provided examples. 
    Focus on problem titles, difficulty levels, topic tags, and companies that have asked these problems. 
    Do not provide specific problem solutions or content."""

    user_prompt = f"Based on the following query, recommend relevant LeetCode problems:\n{user_query}"
    full_prompt = f"{system_prompt}\n\n{few_shot_prompt}\n{user_prompt}\n\nRecommendations:"

    input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to(device)
    attention_mask = torch.ones_like(input_ids)

    with torch.no_grad():
        output = model.generate(
            input_ids,
            attention_mask=attention_mask,
            max_new_tokens=100,  # Adjust as needed
            do_sample=True,
            top_p=0.9,
            temperature=0.7,
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id
        )

    response = tokenizer.decode(output[0], skip_special_tokens=True)
    recommendations = response.split("Recommendations:")[1].strip()
    return recommendations

# Create Gradio interface
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(lines=2, placeholder="Enter your query about LeetCode problems..."),
    outputs="text",
    title="LeetCode Problem Assistant",
    description="Ask about LeetCode problems and get structured responses."
)

# Launch the app
iface.launch(share=True)