app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import spaces  # Import the spaces library

# Model IDs from Hugging Face Hub (now only 1.5B and 7B)
model_ids = {
    "1.5B": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
    "7B": "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
}

# Function to load model and tokenizer (slightly adjusted device_map)
def load_model_and_tokenizer(model_id):
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.bfloat16, # Or torch.float16 if you prefer
        device_map='auto', # Let accelerate decide (will use GPU when @spaces.GPU active)
        trust_remote_code=True
    )
    return model, tokenizer

# Load the selected models and tokenizers
models = {}
tokenizers = {}
for size, model_id in model_ids.items():
    print(f"Loading {size} model: {model_id}")
    models[size], tokenizers[size] = load_model_and_tokenizer(model_id)
    print(f"Loaded {size} model.")

# --- Shared Memory Implementation --- (Same as before)
shared_memory = []

def store_in_memory(memory_item):
    shared_memory.append(memory_item)
    print(f"\n[Memory Stored]: {memory_item[:50]}...")

def retrieve_from_memory(query, top_k=2):
    relevant_memories = []
    query_lower = query.lower()
    for memory_item in shared_memory:
        if query_lower in memory_item.lower():
            relevant_memories.append(memory_item)

    if not relevant_memories:
        print("\n[Memory Retrieval]: No relevant memories found.")
        return []

    print(f"\n[Memory Retrieval]: Found {len(relevant_memories)} relevant memories.")
    return relevant_memories[:top_k]


# --- Swarm Agent Function with Shared Memory (RAG) - DECORATED with @spaces.GPU ---
@spaces.GPU  # <----  GPU DECORATOR ADDED HERE!
def swarm_agent_sequential_rag(user_prompt, temperature=0.7, top_p=0.9, max_new_tokens=300): # Added settings as arguments
    global shared_memory
    shared_memory = [] # Clear memory for each new request

    print("\n--- Swarm Agent Processing with Shared Memory (RAG) - GPU ACCELERATED ---") # Updated message

    # 1.5B Model - Brainstorming/Initial Draft
    print("\n[1.5B Model - Brainstorming] - GPU Accelerated") # Added GPU indication
    retrieved_memory_1_5b = retrieve_from_memory(user_prompt)
    context_1_5b = "\n".join([f"- {mem}" for mem in retrieved_memory_1_5b]) if retrieved_memory_1_5b else "No relevant context found in memory."
    prompt_1_5b = f"Context from Shared Memory:\n{context_1_5b}\n\nYou are a quick idea generator. Generate an initial response to the following user request, considering the context above:\n\nUser Request: {user_prompt}\n\nInitial Response:"
    input_ids_1_5b = tokenizers["1.5B"].encode(prompt_1_5b, return_tensors="pt").to(models["1.5B"].device)
    output_1_5b = models["1.5B"].generate(
        input_ids_1_5b,
        max_new_tokens=max_new_tokens, # Use user-defined max_new_tokens
        temperature=temperature, # Use user-defined temperature
        top_p=top_p,           # Use user-defined top_p
        do_sample=True
    )
    response_1_5b = tokenizers["1.5B"].decode(output_1_5b[0], skip_special_tokens=True)
    print(f"1.5B Response:\n{response_1_5b}")
    store_in_memory(f"1.5B Model Initial Response: {response_1_5b[:200]}...")

    # 7B Model - Elaboration and Detail
    print("\n[7B Model - Elaboration] - GPU Accelerated") # Added GPU indication
    retrieved_memory_7b = retrieve_from_memory(response_1_5b)
    context_7b = "\n".join([f"- {mem}" for mem in retrieved_memory_7b]) if retrieved_memory_7b else "No relevant context found in memory."
    prompt_7b = f"Context from Shared Memory:\n{context_7b}\n\nYou are a detailed elaborator. Take the following initial response and elaborate on it, adding more detail and reasoning, considering the context above. \n\nInitial Response:\n{response_1_5b}\n\nElaborated Response:"
    input_ids_7b = tokenizers["7B"].encode(prompt_7b, return_tensors="pt").to(models["7B"].device)
    output_7b = models["7B"].generate(
        input_ids_7b,
        max_new_tokens=max_new_tokens + 100, # Slightly more tokens for 7B
        temperature=temperature, # Use user-defined temperature
        top_p=top_p,           # Use user-defined top_p
        do_sample=True
    )
    response_7b = tokenizers["7B"].decode(output_7b[0], skip_special_tokens=True)
    print(f"7B Response:\n{response_7b}")
    store_in_memory(f"7B Model Elaborated Response: {response_7b[:200]}...")

    return response_7b # Now returns the 7B model's response as final


# --- Gradio ChatInterface ---
def gradio_interface(message, history, temperature, top_p, max_tokens): # Accept settings from interface
    # history is automatically managed by ChatInterface
    response = swarm_agent_sequential_rag(
        message,
        temperature=temperature,
        top_p=top_p,
        max_new_tokens=int(max_tokens) # Ensure max_tokens is an integer
    )
    return response

iface = gr.ChatInterface( # Using ChatInterface now
    fn=gradio_interface,
    # Define additional inputs for settings
    additional_inputs=[
        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.7, label="Temperature"),
        gr.Slider(minimum=0.01, maximum=1.0, step=0.05, value=0.9, label="Top P"),
        gr.Number(value=300, label="Max Tokens", precision=0), # Use Number for integer tokens
    ],
    title="DeepSeek Agent Swarm Chat (ZeroGPU Demo - 2 Models)", # Updated title
    description="Chat with a DeepSeek agent swarm (1.5B, 7B) with shared memory and adjustable settings. **GPU accelerated using ZeroGPU!** (Requires Pro Space)", # Updated description
)

if __name__ == "__main__":
    iface.launch() # Only launch locally if running this script directly