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

# Model IDs from Hugging Face Hub (now 1.5B, 7B, and 14B)
model_ids = {
    "1.5B": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
    "7B": "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "14B": "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", # Added 14B back
}

# Revised Default Prompts (as defined above)
default_prompt_1_5b = """**Code Analysis Task**
As a Senior Code Analyst, analyze this programming problem:

**User Request:**
{user_prompt}

**Relevant Context:**
{context_1_5b}

**Analysis Required:**
1. Briefly break down the problem, including key constraints and edge cases.
2. Suggest 2-3 potential approach options (algorithms/data structures).
3. Recommend ONE primary strategy and briefly justify your choice. 
4. Provide a very brief initial pseudocode sketch of the core logic.""" 


default_prompt_7b = """**Code Implementation Task**
As a Principal Software Engineer, provide production-ready Streamlit/Python code based on this analysis:

**Initial Analysis:**
{response_1_5b}

**Relevant Context:**
{context_7b}

**Code Requirements:**
1.  Generate concise, production-grade Python code for a Streamlit app.
2.  Include necessary imports, UI elements, and basic functionality.
3.  Add comments for clarity.
    """


# Function to load model and tokenizer (same)
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 (now loads 1.5B, 7B, 14B)
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)
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 Model Swapping ---
@spaces.GPU  # <----  GPU DECORATOR ADDED HERE!
def swarm_agent_sequential_rag(user_prompt, prompt_1_5b_template, prompt_7b_template, final_model_size="7B", temperature=0.5, top_p=0.9, max_new_tokens=300): # Added final_model_size
    global shared_memory
    shared_memory = [] # Clear memory for each new request

    print(f"\n--- Swarm Agent Processing with Shared Memory (RAG) - GPU ACCELERATED - Final Model: {final_model_size} ---") # Updated message

    # 1.5B Model - Brainstorming/Initial Draft (same logic)
    print("\n[1.5B Model - Brainstorming] - GPU Accelerated")
    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."

    # Use user-provided prompt template for 1.5B model
    prompt_1_5b = prompt_1_5b_template.format(user_prompt=user_prompt, context_1_5b=context_1_5b)

    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]}...")

    # Final Stage Model Selection (7B or 14B)
    if final_model_size == "7B":
        final_model = models["7B"]
        final_tokenizer = tokenizers["7B"]
        print("\n[7B Model - Final Code Generation] - GPU Accelerated") # Model-specific message
        model_stage_name = "7B Model - Final Code"
        final_max_new_tokens = max_new_tokens + 100 # Slightly more tokens for 7B

    elif final_model_size == "14B":
        final_model = models["14B"]
        final_tokenizer = tokenizers["14B"]
        print("\n[14B Model - Final Code Generation] - GPU Accelerated") # Model-specific message
        model_stage_name = "14B Model - Final Code"
        final_max_new_tokens = max_new_tokens + 200 # Even more tokens for 14B

    else: # Default to 7B if selection is somehow invalid
        final_model = models["7B"]
        final_tokenizer = tokenizers["7B"]
        print("\n[7B Model - Final Code Generation] - GPU Accelerated (Default)")
        model_stage_name = "7B Model - Final Code (Default)"
        final_max_new_tokens = max_new_tokens + 100


    retrieved_memory_final = retrieve_from_memory(response_1_5b)
    context_final = "\n".join([f"- {mem}" for mem in retrieved_memory_final]) if retrieved_memory_final else "No relevant context found in memory."

    # Use user-provided prompt template for final model (currently using 7B prompt for both 7B and 14B for simplicity, you can create a separate 14B prompt if needed)
    prompt_final = prompt_7b_template.format(response_1_5b=response_1_5b, context_7b=context_final) # Using prompt_7b_template for final stage for now


    input_ids_final = final_tokenizer.encode(prompt_final, return_tensors="pt").to(final_model.device)
    output_final = final_model.generate(
        input_ids_final,
        max_new_tokens=final_max_new_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True
    )
    response_final = final_tokenizer.decode(output_final[0], skip_special_tokens=True)
    print(f"{model_stage_name} Response:\n{response_final}")
    store_in_memory(f"{model_stage_name} Response: {response_final[:200]}...")

    return response_final # Returns final model's response


# --- Gradio ChatInterface --- (with Model Selection Dropdown)
def gradio_interface(message, history, temp, top_p, max_tokens, prompt_1_5b_text, prompt_7b_text, final_model_selector): # Added final_model_selector
    # history is automatically managed by ChatInterface
    response = swarm_agent_sequential_rag(
        message,
        prompt_1_5b_template=prompt_1_5b_text, # Pass prompt templates
        prompt_7b_template=prompt_7b_text,
        final_model_size=final_model_selector, # Pass model selection
        temperature=temp,
        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, prompts, and model selection
    additional_inputs=[
        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature"), # Lowered default temp to 0.5
        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
        gr.Textbox(value=default_prompt_1_5b, lines=10, label="1.5B Model Prompt Template"), # Textbox for 1.5B prompt
        gr.Textbox(value=default_prompt_7b, lines=10, label="7B Model Prompt Template"),   # Textbox for 7B prompt
        gr.Dropdown(choices=["7B", "14B"], value="7B", label="Final Stage Model (7B or 14B)") # Model selection dropdown
    ],
    title="DeepSeek Agent Swarm Chat (ZeroGPU Demo - 2 Models + Model Swap)", # Updated title
    description="Chat with a DeepSeek agent swarm (1.5B + 7B/14B selectable) with shared memory, adjustable settings, **customizable prompts, and model swapping!** **GPU accelerated using ZeroGPU!** (Requires Pro Space)", # Updated description
)

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