import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Set model ID
# comment/uncomment the model you want to use

# GPT-2 (very small, general-purpose, mainly for testing or learning purposes)
# model_id = "gpt2"  

# DeepSeek Coder 1.3B (base version, no instruction fine-tuning — better for raw code generation tasks)
# model_id = "deepseek-ai/deepseek-coder-1.3b"  

# DeepSeek Coder 1.3B Base (same as above — explicit base naming, safe to use)
# model_id = "deepseek-ai/deepseek-coder-1.3b-base"  

# CodeLlama 7B Instruct (powerful code generation model from Meta, instruction-tuned)
# model_id = "codellama/CodeLlama-7b-Instruct-hf"  

# Meta-Llama 3.1 8B Instruct (very powerful general-purpose model, instruction-following, also decent for code & NLP)
# model_id = "meta-llama/Llama-3.1-8B-Instruct"  

# DeepSeek-R1 + Qwen3 8B (highly capable multi-purpose model — great for reasoning, coding, general Q&A)
# model_id = "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B"  

# Qwen2.5-VL-7B Instruct (multimodal: can handle text + images, instruction-tuned — mostly for vision-language tasks)
# model_id = "Qwen/Qwen2.5-VL-7B-Instruct"  

# DeepSeek Coder 1.3B Instruct (great for both natural language and coding tasks)
model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)

# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def generate_code(prompt):
    if not prompt.strip():
        return "⚠ Please enter a valid prompt."

    inputs = tokenizer(prompt, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=200,
            temperature=0.7
        )

    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Strip the prompt if it appears at the start
    if output_text.startswith(prompt):
        output_text = output_text[len(prompt):].lstrip()

    return output_text

demo = gr.Interface(
    fn=generate_code,
    inputs=gr.Textbox(lines=5, label="Ask me a question ? or tell me to generate-code ^_^ :"),
    outputs=gr.Textbox(label="Generated Output is:"),
    title="Code-NLP Fusion"
)

demo.launch()