app.py

import copy
import math
import os
import time
from threading import Thread

import gradio as gr
import spaces
import torch
from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, InputFormat, PdfFormatOption
from langchain.schema.document import Document
from langchain_chroma import Chroma
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_docling import DoclingLoader
from langchain_docling.loader import ExportType
from langchain_text_splitters import RecursiveCharacterTextSplitter
from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache, TextIteratorStreamer
from transformers.models.llama.modeling_llama import rotate_half
import uuid

from utils import (
    calculate_tokens_suggest_compression_ratio,
    repeat_kv,
    update_retrieval_context,
)



# Initialize the model and tokenizer.
api_token = os.getenv("HUGGING_FACE_HUB_TOKEN")
model_name = "meta-llama/Llama-3.1-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token, torch_dtype=torch.float16)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.eval()
model.to(device)
embedding_model = HuggingFaceBgeEmbeddings(
        model_name="BAAI/bge-large-en-v1.5",
        model_kwargs={"device": str(device)},
        encode_kwargs={"normalize_embeddings": True},
        query_instruction=""
    )


# Create a chat template and split into prefix and suffix.
content_system = ""
content_user = "######"
user_template = [
    {"role": "system", "content": content_system},
    {"role": "user", "content": content_user}
]
user = tokenizer.apply_chat_template(user_template, add_generation_prompt=True, tokenize=False)
prefix, suffix = user.split(content_user)
sink_tokens = max(4, len(tokenizer.encode(prefix)))

# Default prompt content.
default_task_description = (
    "Answer the question based on the given passages. "
    "Only give me the answer and do not output any other words."
)
default_few_shot = """Examples
question: Which case was brought to court first Miller v. California or Gates v. Collier ?
answer: Miller v. California
question: The actor that plays Phileas Fogg in "Around the World in 80 Days", co-starred with Gary Cooper in a 1939 Goldwyn Productions film based on a novel by what author?
answer: Charles L. Clifford
question: Prior to playing for Michigan State, Keith Nichol played football for a school located in what city?
answer: Norman
"""

class FinchCache(DynamicCache):
    def __init__(self) -> None:
        super().__init__()
        self.key_cache = []
        self.value_cache = []

    @staticmethod
    def _rotate_half(x):
        x1 = x[..., : x.shape[-1] // 2]
        x2 = x[..., x.shape[-1] // 2 :]
        return torch.cat((-x2, x1), dim=-1)

    def _apply_key_rotary_pos_emb(self, key_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
        return (key_states * cos) + (self._rotate_half(key_states) * sin)

    @staticmethod
    def _rerotate_cos_sin(x, inv_freq, important_pos_batch):
        B, H, L = important_pos_batch.shape
        device = important_pos_batch.device
        device_type = x.device.type
        dtype = x.dtype
        idx = torch.arange(0, L, device=device)
        idx = idx.unsqueeze(0)
        inv_freq = inv_freq[None, None, :, None].float().expand(B, H, -1, 1) # (B, H, M, 1)
        idx = idx[:, None, :].float().expand(B, H, L) # (B, H, L)
        delta_pos =  idx - important_pos_batch
        delta_pos = delta_pos.unsqueeze(2) # (B, H, 1, L)

        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"

        with torch.autocast(device_type=device_type, enabled=False):
            freqs = delta_pos.float() * inv_freq.float()
            freqs = freqs.transpose(2, 3)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos().contiguous()
            sin = emb.sin().contiguous()
        return cos.to(dtype=dtype), sin.to(dtype=dtype)

    @staticmethod
    def gather_important_tokens(states, indices):
        return torch.gather(states, 2, indices.unsqueeze(-1).expand(-1, -1, -1, states.size(3))).contiguous()

    def compress_cache(self, layer_index, important_pos, inv_freq):
        new_length = important_pos.size(2)
        new_cos, new_sin = self._rerotate_cos_sin(self.key_cache[layer_index], inv_freq, important_pos)
        gathered_keys = self.gather_important_tokens(self.key_cache[layer_index], important_pos).clone()
        self.key_cache[layer_index] = self._apply_key_rotary_pos_emb(gathered_keys, new_cos, new_sin)
        gathered_values = self.gather_important_tokens(self.value_cache[layer_index], important_pos).clone()
        self.value_cache[layer_index] = gathered_values
        self._seen_tokens = new_length

    def save(self, path: str):
        """Save the cache to disk, moving tensors to CPU."""
        try:
            os.makedirs(os.path.dirname(path), exist_ok=True)
            torch.save(
                {"key_cache": [k.cpu() for k in self.key_cache], "value_cache": [v.cpu() for v in self.value_cache]},
                path,
            )
        except Exception as e:
            print(f"Error occurred while saving: {e}")

    @classmethod
    def load(cls, path: str, device: str = "cpu") -> "FinchCache":
        """Load the cache from disk and move tensors to the specified device."""
        data = torch.load(path, map_location=device)
        cache = cls()
        cache.key_cache = [k.to(device) for k in data["key_cache"]]
        cache.value_cache = [v.to(device) for v in data["value_cache"]]
        cache._seen_tokens = cache.value_cache[0].size(2) if cache.value_cache else 0
        return cache



def convert_to_markdown(file_objs, url, do_ocr, do_table_structure):
    file_path = file_objs if file_objs is not None else url
    pipeline_options = PdfPipelineOptions()
    pipeline_options.do_ocr = do_ocr
    pipeline_options.do_table_structure = do_table_structure
    pdf_format_options = PdfFormatOption(
        pipeline_options=pipeline_options,
        backend=PyPdfiumDocumentBackend,
    )
    doc_converter = DocumentConverter(
        allowed_formats=[InputFormat.PDF],
        format_options={
            InputFormat.PDF: pdf_format_options
        }
    )

    # Pass the custom converter to the DoclingLoader.
    loader = DoclingLoader(
        file_path=file_path,
        export_type=ExportType.MARKDOWN,
        converter=doc_converter
    )
    docs = loader.load()
    return docs[0].page_content


def create_rag_index(collection_name, text_no_prefix):
    """Loads the PDF, splits its text, and builds a vectorstore for naive RAG."""
    text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
                tokenizer,
                chunk_size=256,
                chunk_overlap=0,
                add_start_index=True,
                strip_whitespace=True,
                separators=["\n\n", "\n", ".", " ", ""],
            )
    # Concatenate pages and create Document objects.
    docs = [Document(page_content=x) for x in text_splitter.split_text(text_no_prefix)]
    vectorstore = Chroma.from_documents(collection_name=collection_name, persist_directory="./chroma_db", documents=docs, embedding=embedding_model)
    return vectorstore


@spaces.GPU
def auto_convert(file_objs, url, do_ocr, do_table_structure):
    if file_objs is None and (url is None or url.strip() == ""):
        return (
            gr.update(value=""),
            "Number of tokens before compression: ",
            gr.update(), 
            "Number of tokens after compression: ",
            0,
            gr.update(interactive=False),  # Disable compress button when no input.
            False,
            {}  # return an empty state dictionary
        )
    # Convert the document to markdown.
    print("Converting to markdown")
    markdown = convert_to_markdown(file_objs, url, do_ocr, do_table_structure)
    print("Done")
    combined_text = prefix + markdown
    print("Suggestioning Compression ratio")
    token_count, suggestions, _ = calculate_tokens_suggest_compression_ratio(combined_text, tokenizer, model)
    print("Done")
    min_ratio = min(suggestions)
    max_ratio = max(suggestions)
    default_ratio = suggestions[len(suggestions) // 2]
    retrieval_tokens = int(token_count / default_ratio)
    token_count_str = f"Number of tokens before compression: {token_count}"
    retrieval_str = f"Number of tokens after compression: {retrieval_tokens}"
    slider_update = gr.update(value=default_ratio, minimum=min_ratio, maximum=max_ratio, step=1)
    
    # Create the RAG index immediately.
    if combined_text.startswith(prefix):
        rag_text = combined_text[len(prefix):]
    else:
        rag_text = combined_text
    collection_name = "default_collection_" + uuid.uuid4().hex[:6]
    rag_index = create_rag_index(collection_name, rag_text)
    state = {"rag_index": collection_name}
    print("Done")
    
    return (
        combined_text, 
        token_count_str, 
        slider_update, 
        retrieval_str, 
        token_count, 
        gr.update(interactive=True),
        False,
        state
    )
    

def get_compressed_kv_cache(sink_tokens, step_size, target_token_size, context_ids, context_attention_mask, question_ids, question_attention_mask):
    device = model.device
    dtype = model.dtype
    sink_tokens = sink_tokens
    num_chunks = step_size
    context_ids = context_ids.to(device)
    context_attention_mask = context_attention_mask.to(device)
    question_ids = question_ids.to(device)
    question_attention_mask = question_attention_mask.to(device)
    question_len = question_ids.size(1)
    total_len = context_ids.size(1)
    max_context_tokens_allowed = model.config.max_position_embeddings - question_len
    if total_len > max_context_tokens_allowed:
        num_chunks = max(step_size, math.ceil(total_len / max_context_tokens_allowed))

    if total_len <= sink_tokens or num_chunks == 1:
        # If the context is too short or only one chunk is desired, use the entire context.
        context_ids_list = [context_ids]
        context_attention_mask_list = [context_attention_mask]
    else:
        # Calculate how many tokens remain after the sink tokens.
        remainder_len = total_len - sink_tokens

        # Compute the base tokens per chunk and any leftover.
        base = remainder_len // num_chunks
        leftover = remainder_len % num_chunks

        # Build a list of chunk sizes.
        # First chunk gets the sink tokens plus base tokens.
        chunk_sizes = [sink_tokens + base]

        # Chunks 2 to num_chunks-1 get base tokens each.
        for _ in range(num_chunks - 2):
            chunk_sizes.append(base)

        # The last chunk gets the remaining tokens (base + leftover).
        if num_chunks > 1:
            chunk_sizes.append(base + leftover)

        # Now slice the context using the calculated sizes.
        context_ids_list = []
        context_attention_mask_list = []
        offset = 0
        for size in chunk_sizes:
            end = offset + size
            context_ids_list.append(context_ids[:, offset:end])
            context_attention_mask_list.append(context_attention_mask[:, offset:end])
            offset = end

    # (Optional) Continue with the rest of your processing…
    len_rest = max(total_len - sink_tokens, 1)
    compression_factor = len_rest // target_token_size
    if compression_factor < 1:
        compression_factor = 1

    tokenized_doc_chunks = []
    for ids_chunk, mask_chunk in zip(context_ids_list, context_attention_mask_list):
        tokenized_doc_chunks.append({"input_ids": ids_chunk, "attention_mask": mask_chunk})

    print("Number of chunks: ", len(tokenized_doc_chunks))

    rotary_emb = model.model.rotary_emb.to(device)
    inv_freq = rotary_emb.inv_freq
    batch_size = question_ids.size(0)
    ones_mask = torch.ones(batch_size, 1, dtype=question_attention_mask.dtype, device=device)

    cache = FinchCache()
    past_cache_len = 0
    past_attention_mask = torch.zeros(batch_size, 0, dtype=question_attention_mask.dtype, device=device)
    num_chunks = len(tokenized_doc_chunks)

    # Prepare a shared dictionary for hook outputs.
    query_context_matrices = {}

    # Define a hook function that uses a per-chunk offset stored on self.
    def query_hook_fn(module, input, output):
        layer_idx = getattr(module, "layer_idx", None)
        if layer_idx is not None:
            query_states = output.detach()
            bsz, seq_len, hidden_dim = query_states.size()
            num_query_heads = module.num_query_heads
            head_dim = hidden_dim // num_query_heads
            query_states = (
                query_states.view(bsz, seq_len, num_query_heads, head_dim)
                .transpose(1, 2)
                .contiguous()
            )
            # Use self._current_chunk_offset to select only the new tokens.
            query_context_matrices[layer_idx] = query_states[:, :, _current_chunk_offset:, :].clone()

    # Pre-register hooks for all layers only once.
    hooks = []
    for i, layer in enumerate(model.model.layers):
        layer.self_attn.q_proj.layer_idx = i  # For tracking.
        layer.self_attn.q_proj.num_query_heads = layer.self_attn.config.num_attention_heads
        hook = layer.self_attn.q_proj.register_forward_hook(query_hook_fn)
        hooks.append(hook)

    # Process each document chunk sequentially.
    for j, tokenized_doc_chunk in enumerate(tokenized_doc_chunks):
        current_seq_length = tokenized_doc_chunk["input_ids"].size(1)
        # Save the offset in an attribute the hook can access.
        _current_chunk_offset = current_seq_length
        # Clear the dictionary from any previous chunk.
        query_context_matrices.clear()

        # These chunks are already on the device.
        chunk_input_ids = tokenized_doc_chunk["input_ids"].contiguous()
        chunk_attention_mask = tokenized_doc_chunk["attention_mask"].contiguous()
        segment_attention_mask = torch.cat(
            [past_attention_mask, chunk_attention_mask, ones_mask], dim=-1
        ).contiguous()
        current_input_ids = torch.cat([chunk_input_ids, question_ids], dim=-1).contiguous()
        current_attention_mask = torch.cat([segment_attention_mask, question_attention_mask], dim=-1).contiguous()

        past_seen_tokens = cache.get_seq_length() if cache is not None else 0
        cache_position = torch.arange(
            past_seen_tokens + chunk_input_ids.shape[1],
            past_seen_tokens + current_input_ids.shape[1],
            device=device
        )
        causal_mask = model.model._prepare_4d_causal_attention_mask_with_cache_position(
            current_attention_mask,
            sequence_length=question_ids.size(1),
            target_length=current_attention_mask.size(-1),
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=current_input_ids.size(0),
        ).contiguous()

        with torch.no_grad():
            outputs = model.model(
                input_ids=current_input_ids,
                use_cache=True,
                past_key_values=cache,
            )
            cache = outputs.past_key_values

        len_question = question_ids.size(1)
        # Now, for each transformer layer, update the cache using the query/key attention.
        for layer_idx in range(len(model.model.layers)):
            key_matrix = cache.key_cache[layer_idx]
            query_matrix = query_context_matrices[layer_idx]
            layer_cache_pos = torch.arange(
                past_cache_len + current_seq_length,
                past_cache_len + current_seq_length + len_question,
                device=device
            )
            position_ids = layer_cache_pos.unsqueeze(0)
            cos, sin = rotary_emb(query_matrix, position_ids)
            cos = cos.unsqueeze(1)
            sin = sin.unsqueeze(1)
            query_matrix = (query_matrix * cos) + (rotate_half(query_matrix) * sin)
            num_repeats = model.config.num_attention_heads // model.config.num_key_value_heads
            key_matrix = repeat_kv(key_matrix, num_repeats)

            scaling = math.sqrt(model.config.head_dim)
            attention_matrix = torch.matmul(query_matrix, key_matrix.transpose(2, 3)) / scaling
            causal_mask_sliced = causal_mask[:, :, :, : key_matrix.shape[-2]]
            attention_matrix = attention_matrix + causal_mask_sliced
            attention_matrix = torch.nn.functional.softmax(attention_matrix, dim=-1, dtype=torch.float32).to(query_matrix.dtype)
            # Normalization
            tol = 1e-8
            binary_mask = (torch.abs(causal_mask_sliced.to(torch.float32)) < tol).to(torch.float32)
            non_zero_counts = binary_mask.sum(dim=3, keepdim=True)
            non_zero_counts = torch.clamp_min(non_zero_counts, 1.0).to(attention_matrix.dtype)
            attention_matrix = attention_matrix / non_zero_counts
            if j != num_chunks - 1: 
                attention_matrix = attention_matrix[:, :, :, : past_cache_len + current_seq_length].clone().contiguous()
            else:
                attention_matrix = attention_matrix[:, :, :, : past_cache_len + current_seq_length + len_question].clone().contiguous()
            attention_matrix = torch.sum(attention_matrix, dim=-2)
            attention_matrix = attention_matrix.view(
                attention_matrix.size(0), model.config.num_key_value_heads, num_repeats, -1
            ).sum(dim=2)
            full_context_size = attention_matrix.size(-1)
            attention_matrix[..., :sink_tokens] = float("inf")
            if j == num_chunks - 1:
                attention_matrix[..., -len_question:] = float("inf")
            if j == 0:
                k = int(sink_tokens + (max(0, current_seq_length - sink_tokens) // compression_factor))
                k = min(k + past_cache_len, full_context_size)
            elif j < num_chunks - 1:
                to_keep_new = int(current_seq_length // compression_factor)
                k = min(past_cache_len + to_keep_new, full_context_size)
            else:
                desired_final = sink_tokens + target_token_size + len_question# TODO remember to include the question tokens
                k = desired_final if full_context_size >= desired_final else full_context_size
            k = max(k, sink_tokens)
            selected_indices = torch.topk(attention_matrix, k, dim=-1).indices
            selected_indices, _ = torch.sort(selected_indices, dim=-1)
            cache.compress_cache(layer_idx, selected_indices, inv_freq)

        past_cache_len = cache._seen_tokens
        past_attention_mask = torch.ones(1, past_cache_len, device=device)

    # Remove the hooks once after all chunks are processed.
    for hook in hooks:
        hook.remove()

    return cache


def run_naive_rag_query(collection_name, query, rag_token_size, prefix, task, few_shot_examples):
    """
    For naive RAG, retrieves top-k chunks (k based on target token size)
    and generates an answer using those chunks.
    """
    k = max(1, rag_token_size // 256)
    vectorstore = Chroma(persist_directory="./chroma_db", embedding=embedding_model, collection_name=collection_name)
    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": k})
    retrieved_docs = retriever.invoke(query)
    for doc in retrieved_docs:
        print("=================")
        print(doc.page_content)
        print("=================")
    formatted_context = "\n\n".join([doc.page_content for doc in retrieved_docs])
    
    rag_context = prefix + "Retrieved context: \n" + formatted_context + task + few_shot_examples
    
    return rag_context


@spaces.GPU
def prepare_compression_and_rag(combined_text, retrieval_slider_value, global_local_value, task_description, few_shot, state):
    """
    Prepares the compressed KV cache. Uses the precomputed rag_index from state.
    """
    percentage = int(global_local_value.replace('%', ''))
    question_text = task_description + "\n" + few_shot
    context_encoding = tokenizer(combined_text, return_tensors="pt").to(device)
    question_encoding = tokenizer(question_text, return_tensors="pt").to(device)
    context_ids = context_encoding["input_ids"]
    context_attention_mask = context_encoding["attention_mask"]
    question_ids = question_encoding["input_ids"]
    question_attention_mask = question_encoding["attention_mask"]
    retrieval_context_length = int(context_ids.size(1) / retrieval_slider_value)
    
    if percentage > 0:
        target_token_size = int(retrieval_context_length * (percentage / 100))
        print("Target token size for compression: ", target_token_size)
        step_size = 2
        start_time_prefill = time.perf_counter()
        past_key_values = copy.deepcopy(get_compressed_kv_cache(sink_tokens, step_size, target_token_size,
                                                                context_ids, context_attention_mask,
                                                                question_ids, question_attention_mask))
        compressed_length = past_key_values.get_seq_length()
        print("Context size after compression: ", compressed_length)
        print("Compression rate: ", context_ids.size(1) / compressed_length)
    else:
        start_time_prefill = 0
        target_token_size = 0
        past_key_values = FinchCache()
        compressed_length = past_key_values.get_seq_length()

    cache_name = "default_cache_" + uuid.uuid4().hex[:6]
    cache_name = "default_cache_" + uuid.uuid4().hex[:6] + ".pt"
    save_dir = "./cache_dir"
    os.makedirs(save_dir, exist_ok=True)
    save_path = os.path.join(save_dir, cache_name)
    past_key_values.save(save_path)
        
    # Use the precomputed rag_index from state.
    collection_name = state.get("rag_index", None)
    if collection_name is None:
        print("Collection name not found creating a new one.")
        if combined_text.startswith(prefix):
            rag_text = combined_text[len(prefix):]
        else:
            rag_text = combined_text
        collection_name = "default_collection_" + uuid.uuid4().hex[:6]
        rag_index = create_rag_index(collection_name, rag_text)

    state.update({
        "compressed_cache": save_path,
        "compressed_length": compressed_length,
        "rag_index": collection_name,
        "target_token_size": target_token_size,
        "global_local": percentage,
        "combined_text": combined_text,
        "task_description": task_description,
        "few_shot": few_shot,
        "retrieval_slider": retrieval_context_length,
        "prefill_time": time.perf_counter() - start_time_prefill
    })
    return state, True


@spaces.GPU
def chat_response_stream(message: str, history: list, state: dict):
    """
    Generates a chat response with streaming output.
    Returns a simple string (not a list of message dicts) for ChatInterface.
    """
    user_message = message
    save_path = state["compressed_cache"]
    past_key_values = FinchCache.load(save_path, device=model.device)
    try:
        os.remove(save_path)
    except Exception as e:
        print(f"Error removing cache file: {e}")
    compressed_length = past_key_values.get_seq_length()
    collection_name = state["rag_index"]
    retrieval_slider_value = state["retrieval_slider"]
    percentage = state["global_local"]
    
    rag_retrieval_size = int(retrieval_slider_value * (1.0 - (percentage / 100)))
    print("RAG retrieval size: ", rag_retrieval_size)
    
    if percentage == 0:
        rag_prefix = prefix
        rag_task = state["task_description"]
        rag_few_shot = state["few_shot"]
    else:
        rag_prefix = ""
        rag_task = ""
        rag_few_shot = ""
    print("user message: ", user_message)
    if rag_retrieval_size != 0:
        print("Running RAG query")
        rag_context = run_naive_rag_query(collection_name, user_message, rag_retrieval_size, rag_prefix, rag_task, rag_few_shot)
        new_input = rag_context + "\nquestion: " + user_message + suffix + "answer:"
    else:
        new_input = "\nquestion: " + user_message + suffix + "answer:"
    tokenized_new_input = tokenizer(new_input, return_tensors="pt").to(device)
    eos_block = torch.full((1, compressed_length), tokenizer.eos_token_id, device=device, dtype=torch.long)
    new_input_ids = torch.cat([eos_block, tokenized_new_input["input_ids"]], dim=-1)
    new_attention_mask = torch.cat([torch.ones((1, compressed_length), device=device), tokenized_new_input["attention_mask"]], dim=-1)
    
    print("New input is: ", new_input)
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids=new_input_ids,
        attention_mask=new_attention_mask,
        past_key_values=past_key_values,
        streamer=streamer,
        use_cache=True,
        max_new_tokens=1024,
        num_beams=1,
        do_sample=False,
        temperature=1.0,
        top_p=1.0,
        top_k=None,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    
    full_output = ""
    for text in streamer:
        full_output += text
        time.sleep(0.05)
        yield full_output
        
    state["compressed_cache"] = past_key_values
    return full_output

##########################################################################
# Gradio Interface: note that we now use ChatInterface instead of a Chatbot.
##########################################################################
CSS = """
body {
    font-family: "Times New Roman", Times, serif;
}
.upload-section {
    padding: 10px;
    border: 2px dashed #ccc;
    border-radius: 10px;
}
.upload-button {
    background: #34c759 !important;
    color: white !important;
    border-radius: 25px !important;
}
.chatbot-container {
    margin-top: 20px;
}
.status-output {
    margin-top: 10px;
    font-size: 14px;
}
.processing-info {
    margin-top: 5px;
    font-size: 12px;
    color: #666;
}
.info-container {
    margin-top: 10px;
    padding: 10px;
    border-radius: 5px;
}
.file-list {
    margin-top: 0;
    max-height: 200px;
    overflow-y: auto;
    padding: 5px;
    border: 1px solid #eee;
    border-radius: 5px;
}
.stats-box {
    margin-top: 10px;
    padding: 10px;
    border-radius: 5px;
    font-size: 12px;
}
.submit-btn {
    background: #1a73e8 !important;
    color: white !important;
    border-radius: 25px !important;
    margin-left: 10px;
    padding: 5px 10px;
    font-size: 16px;
}
.input-row {
    display: flex;
    align-items: center;
}
@media (min-width: 768px) {
    .main-container {
        display: flex;
        justify-content: space-between;
        gap: 20px;
    }
    .upload-section {
        flex: 3;
    }
    .chatbot-container {
        flex: 1;
        margin-top: 0;
    }
}
"""

with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
    gr.HTML("<h1><center>Beyond RAG with LLama 3.1-8B-Instruct Model</center></h1>")
    gr.HTML("<center><p>Compress your document and chat with it.</p></center>")
    
    hidden_token_count = gr.State(value=0)
    compression_done = gr.State(value=False)
    compressed_doc_state = gr.State(value={})
    
    with gr.Row(elem_classes="main-container"):
        with gr.Column(elem_classes="upload-section"):
            gr.Markdown("## Document Preprocessing")
            with gr.Row():
                file_input = gr.File(label="Drop file here or upload", file_count="multiple", elem_id="file-upload-area")
                url_input = gr.Textbox(label="or enter a URL", placeholder="https://example.com/document.pdf")
            with gr.Row():
                do_ocr = gr.Checkbox(label="Do OCR", value=False)
                do_table = gr.Checkbox(label="Include Table Structure", value=False)
            with gr.Accordion("Prompt Designer", open=False):
                task_description_input = gr.Textbox(label="Task Description", value=default_task_description, lines=3, elem_id="task-description")
                few_shot_input = gr.Textbox(label="Few-Shot Examples", value=default_few_shot, lines=10, elem_id="few-shot")
            with gr.Accordion("Show Markdown Output", open=False):
                markdown_output = gr.Textbox(label="Markdown Output", lines=20)
            token_count_text = gr.Markdown("Number of tokens before compression: ")
            retrieval_slider = gr.Slider(label="Select Compression Rate", minimum=1, maximum=32, step=1, value=2)
            retrieval_info_text = gr.Markdown("Number of tokens after compression: ")
            global_local_slider = gr.Radio(label="Global vs Local (0 is all RAG, 100 is all global)", 
                                           choices=["0%", "25%", "50%", "75%", "100%"], value="75%")
            compress_button = gr.Button("Compress Document", interactive=False, elem_classes="upload-button")
            
            file_input.change(
                fn=auto_convert,
                inputs=[file_input, url_input, do_ocr, do_table],
                outputs=[markdown_output, token_count_text, retrieval_slider, retrieval_info_text, hidden_token_count, compress_button, compression_done, compressed_doc_state]
            )
            url_input.change(
                fn=auto_convert,
                inputs=[file_input, url_input, do_ocr, do_table],
                outputs=[markdown_output, token_count_text, retrieval_slider, retrieval_info_text, hidden_token_count, compress_button, compression_done, compressed_doc_state]
            )
            do_ocr.change(
                fn=auto_convert,
                inputs=[file_input, url_input, do_ocr, do_table],
                outputs=[markdown_output, token_count_text, retrieval_slider, retrieval_info_text, hidden_token_count, compress_button, compression_done, compressed_doc_state]
            )
            do_table.change(
                fn=auto_convert,
                inputs=[file_input, url_input, do_ocr, do_table],
                outputs=[markdown_output, token_count_text, retrieval_slider, retrieval_info_text, hidden_token_count, compress_button, compression_done, compressed_doc_state]
            )
            retrieval_slider.change(
                fn=update_retrieval_context,
                inputs=[hidden_token_count, retrieval_slider],
                outputs=retrieval_info_text
            )
            compress_button.click(
                fn=prepare_compression_and_rag,
                inputs=[markdown_output, retrieval_slider, global_local_slider, task_description_input, few_shot_input, compressed_doc_state],
                outputs=[compressed_doc_state, compression_done]
            )
        
        with gr.Column(elem_classes="chatbot-container"):
            gr.Markdown("## Chat")
            chat_interface = gr.ChatInterface(
                fn=chat_response_stream,
                additional_inputs=[compressed_doc_state],
                type="messages"
            )

demo.queue(max_size=16).launch()