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# app.py
from transformers import AutoTokenizer, AutoModelForCausalLM
import requests
import gradio as gr
import torch
# Load the Hugging Face model and tokenizer in 8-bit precision
model_name = "gpt2" # Smaller and faster model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map="auto")
# 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 Hugging Face model to generate code
inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Move inputs to GPU
outputs = model.generate(**inputs, max_length=300) # Reduced max_length
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
# Gradio interface for the app
def generate_contract(language, requirements):
return generate_smart_contract(language, requirements)
interface = gr.Interface(
fn=generate_contract,
inputs=["text", "text"],
outputs="text",
title="Smart Contract Generator",
description="Generate smart contracts using AI."
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch() |