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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
print("🚀 Loading DeepSeek-Coder-1.3B model...")
model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=torch.float32
)
print("✅ Model loaded successfully!")
def generate_code(prompt, max_length=512, temperature=0.7):
# Fixed: Using proper string concatenation instead of f-string
instruction_part = "### Instruction:"
response_part = ""
formatted_prompt = instruction_part + prompt + response_part
inputs = tokenizer(formatted_prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_length,
temperature=temperature,
do_sample=True,
top_p=0.95,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the generated response
if "### Response:" in response:
return response.split("### Response:")[-1].strip()
return response
demo = gr.Interface(
fn=generate_code,
inputs=[
gr.Textbox(
label="Coding Prompt",
placeholder="Enter your coding question or request...",
lines=5
),
gr.Slider(
minimum=128,
maximum=1024,
value=512,
step=64,
label="Max Length"
),
gr.Slider(
minimum=0.1,
maximum=2.0,
value=0.7,
step=0.1,
label="Temperature"
)
],
outputs=gr.Textbox(label="Generated Code", lines=15),
title="🚀 DeepSeek Coder 1.3B - Docker Edition",
description="AI coding assistant powered by DeepSeek-Coder-1.3B running in Docker",
examples=[
["Write a Python function for binary search"],
["Create a Flask REST API for user login"],
["Write a JavaScript function to debounce API calls"],
["Create a SQL query to find top 10 users by activity"]
],
theme=gr.themes.Soft()
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
show_error=True
) |