app.py

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.")