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# app.py
from transformers import AutoTokenizer, AutoModelForCausalLM
import requests
import gradio as gr
import torch
# Load the CodeParrot model and tokenizer (only once)
model_name = "codeparrot/codeparrot-small" # CodeParrot model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Groq API configuration
GROQ_API_KEY = "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"
GROQ_API_URL = "https://api.groq.com/v1/completions"
# Function to query Groq API
def query_groq(prompt):
headers = {
"Authorization": f"Bearer {GROQ_API_KEY}",
"Content-Type": "application/json"
}
data = {
"prompt": prompt,
"max_tokens": 150
}
response = requests.post(GROQ_API_URL, headers=headers, json=data)
return response.json()["choices"][0]["text"]
# Function to generate smart contract code
def generate_smart_contract(language, requirements):
# Create a prompt for the model
prompt = f"Generate a {language} smart contract with the following requirements: {requirements}"
# Use the CodeParrot model to generate code
inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
outputs = model.generate(**inputs, max_length=300) # Increased max_length for better results
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Enhance the code using Groq API
enhanced_code = query_groq(generated_code)
return enhanced_code
# Custom CSS for a 3D CGI Figma-like feel
custom_css = """
body {
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
color: #fff;
perspective: 1000px;
overflow: hidden;
}
.gradio-container {
background: rgba(255, 255, 255, 0.1);
border-radius: 15px;
padding: 20px;
box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
backdrop-filter: blur(10px);
border: 1px solid rgba(255, 255, 255, 0.3);
transform-style: preserve-3d;
transform: rotateY(0deg) rotateX(0deg);
transition: transform 0.5s ease;
}
.gradio-container:hover {
transform: rotateY(10deg) rotateX(10deg);
}
.gradio-input, .gradio-output {
background: rgba(255, 255, 255, 0.2);
border: none;
border-radius: 10px;
padding: 10px;
color: #fff;
transform-style: preserve-3d;
transition: transform 0.3s ease;
}
.gradio-input:focus, .gradio-output:focus {
background: rgba(255, 255, 255, 0.3);
outline: none;
transform: translateZ(20px);
}
.gradio-button {
background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
border: none;
border-radius: 10px;
color: #fff;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
transition: background 0.3s ease, transform 0.3s ease;
transform-style: preserve-3d;
}
.gradio-button:hover {
background: linear-gradient(135deg, #2575fc 0%, #6a11cb 100%);
transform: translateZ(10px);
}
h1 {
text-align: center;
font-size: 2.5em;
margin-bottom: 20px;
color: white; /* White title color */
transform-style: preserve-3d;
transform: translateZ(30px);
}
@keyframes float {
0% {
transform: translateY(0) translateZ(0);
}
50% {
transform: translateY(-10px) translateZ(10px);
}
100% {
transform: translateY(0) translateZ(0);
}
}
.gradio-container {
animation: float 4s ease-in-out infinite;
}
"""
# Gradio interface for the app
def generate_contract(language, requirements):
return generate_smart_contract(language, requirements)
# Dropdown options for programming languages
languages = ["Solidity", "Vyper", "Rust", "JavaScript", "Python"]
interface = gr.Interface(
fn=generate_contract,
inputs=[
gr.Dropdown(label="Programming Language", choices=languages, value="Solidity"), # Dropdown menu
gr.Textbox(label="Requirements", placeholder="e.g., ERC20 token with minting functionality")
],
outputs=gr.Textbox(label="Generated Smart Contract"),
title="Smart Contract Generator",
description="Generate smart contracts using AI.",
css=custom_css
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch() |