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60c2e3a 9345df8 07ee8fe 19c7c87 7545181 f0d1ea9 60c2e3a 9345df8 60c2e3a 07ee8fe a1094ec f0d1ea9 7545181 07ee8fe 7545181 f0d1ea9 7545181 9345df8 19c7c87 9345df8 a1094ec 9345df8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import os
import streamlit as st
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import subprocess
# Force CPU usage & prevent model download issues
os.environ["HF_HOME"] = "./cache" # Store model locally
MODEL_NAME = "Salesforce/codegen-350M-mono" # Updated model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
def generate_code(description, language):
prompt = f"Generate {language} code: {description}"
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs, max_length=400)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response.strip()
def execute_code(code, language):
if language == "Python":
try:
result = subprocess.run(['python3', '-c', code], capture_output=True, text=True, timeout=5)
return result.stdout if result.stdout else result.stderr
except Exception as e:
return str(e)
return "Code execution only supported for Python."
# Streamlit UI
st.title("Multi-Language Text-to-Code AI")
st.write("Convert natural language descriptions into code in different programming languages! Run Python code directly in the app.")
description = st.text_area("Describe your coding task...")
language = st.selectbox("Select Programming Language", ["Python", "JavaScript", "Java"])
if st.button("Generate Code"):
if description:
code = generate_code(description, language)
st.code(code, language=language.lower())
if language == "Python":
output = execute_code(code, language)
st.text_area("Execution Output", output, height=150)
else:
st.warning("Please enter a description to generate code.")
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