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import streamlit as st
import requests
import os # To access environment variables
import google.generativeai as genai # Import Gemini API
# Load API keys from environment variables
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")
# Set up Hugging Face API
MODEL_ID = "Salesforce/codet5p-770m" # CodeT5+ (Recommended)
API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
# Initialize Gemini API
genai.configure(api_key='AIzaSyBkc8CSEhyYwZAuUiJfzF1Xtns-RYmBOpg')
def translate_code(code_snippet, source_lang, target_lang):
"""Translate code using Hugging Face API."""
prompt = f"Translate the following {source_lang} code to {target_lang}:\n\n{code_snippet}\n\nTranslated {target_lang} Code:\n"
response = requests.post(API_URL, headers=HEADERS, json={
"inputs": prompt,
"parameters": {
"max_new_tokens": 150,
"temperature": 0.2,
"top_k": 50
}
})
if response.status_code == 200:
generated_text = response.json()[0]["generated_text"]
translated_code = generated_text.split(f"Translated {target_lang} Code:\n")[-1].strip()
return translated_code
else:
return f"Error: {response.status_code}, {response.text}"
def fallback_translate_with_gemini(code_snippet, source_lang, target_lang):
"""Fallback function using Gemini API for translation."""
prompt = f"""You are a code translation expert. Convert the following {source_lang} code to {target_lang}:
{code_snippet}
Ensure the translation is accurate and follows {target_lang} best practices.
Do not give any explaination. only give the translated code.
"""
try:
model = genai.GenerativeModel("gemini-1.5-pro")
response = model.generate_content(prompt)
return response.text.strip() if response else "Translation failed."
except Exception as e:
return f"Gemini API Error: {str(e)}"
# Streamlit UI
st.title("🔄 Programming Language Translator")
st.write("Translate code between different programming languages using AI.")
languages = ["Python", "Java", "C++", "C"]
source_lang = st.selectbox("Select source language", languages)
target_lang = st.selectbox("Select target language", languages)
code_input = st.text_area("Enter your code here:", height=200)
# Initialize session state
if "translate_attempts" not in st.session_state:
st.session_state.translate_attempts = 0
st.session_state.translated_code = ""
if st.button("Translate"):
if code_input.strip():
st.session_state.translate_attempts += 1
with st.spinner("Translating..."):
if st.session_state.translate_attempts == 1:
# First attempt using the pretrained model
st.session_state.translated_code = translate_code(code_input, source_lang, target_lang)
else:
# Second attempt uses Gemini API
st.session_state.translated_code = fallback_translate_with_gemini(code_input, source_lang, target_lang)
st.subheader("Translated Code:")
st.code(st.session_state.translated_code, language=target_lang.lower())
else:
st.warning("⚠️ Please enter some code before translating.")
# V1 without gemini api
# import streamlit as st
# import requests
# import os # Import os to access environment variables
# # Get API token from environment variable
# API_TOKEN = os.getenv("HF_API_TOKEN")
# # Change MODEL_ID to a better model
# MODEL_ID = "Salesforce/codet5p-770m" # CodeT5+ (Recommended)
# # MODEL_ID = "bigcode/starcoder2-15b" # StarCoder2
# # MODEL_ID = "bigcode/starcoder"
# API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
# HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
# def translate_code(code_snippet, source_lang, target_lang):
# """Translate code using Hugging Face API securely."""
# prompt = f"Translate the following {source_lang} code to {target_lang}:\n\n{code_snippet}\n\nTranslated {target_lang} Code:\n"
# response = requests.post(API_URL, headers=HEADERS, json={
# "inputs": prompt,
# "parameters": {
# "max_new_tokens": 150,
# "temperature": 0.2,
# "top_k": 50
# # "stop": ["\n\n", "#", "//", "'''"]
# }
# })
# if response.status_code == 200:
# generated_text = response.json()[0]["generated_text"]
# translated_code = generated_text.split(f"Translated {target_lang} Code:\n")[-1].strip()
# return translated_code
# else:
# return f"Error: {response.status_code}, {response.text}"
# # Streamlit UI
# st.title("🔄 Code Translator using StarCoder")
# st.write("Translate code between different programming languages using AI.")
# languages = ["Python", "Java", "C++", "C"]
# source_lang = st.selectbox("Select source language", languages)
# target_lang = st.selectbox("Select target language", languages)
# code_input = st.text_area("Enter your code here:", height=200)
# if st.button("Translate"):
# if code_input.strip():
# with st.spinner("Translating..."):
# translated_code = translate_code(code_input, source_lang, target_lang)
# st.subheader("Translated Code:")
# st.code(translated_code, language=target_lang.lower())
# else:
# st.warning("⚠️ Please enter some code before translating.") |