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code-translator
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import streamlit as st
import requests
import os
import google.generativeai as genai
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import TextVectorization

# --- Config ---
vocab_size = 10000
sequence_length = 150

# Load API keys
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")

# Hugging Face setup
MODEL_ID = "Salesforce/codet5p-770m"
API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}

genai.configure(api_key="AIzaSyBkc8CSEhyYwZAuUiJfzF1Xtns-RYmBOpg")

# --- Load Local Model & Vectorizers ---
model = tf.keras.models.load_model("java_to_python_seq2seq_model.h5")

java_vectorizer = TextVectorization(max_tokens=vocab_size, output_sequence_length=sequence_length)
python_vectorizer = TextVectorization(max_tokens=vocab_size, output_sequence_length=sequence_length)

# Dummy adaptation to initialize
java_vectorizer.adapt(tf.data.Dataset.from_tensor_slices(["public class Main { public static void main(String[] args) {} }"]))
python_vectorizer.adapt(tf.data.Dataset.from_tensor_slices(["def main():\n    pass"]))

python_vocab = python_vectorizer.get_vocabulary()
index_to_word = dict(enumerate(python_vocab))

# --- Translator Functions ---

def fallback_translate_with_gemini(code_snippet, source_lang, target_lang):
    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 explanation. 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)}"

def translate_with_local_model(code_snippet):
    try:
        java_seq = java_vectorizer(tf.constant([code_snippet]))
        python_in = tf.constant([[1] + [0] * (sequence_length - 1)])
        translated_tokens = []

        for i in range(sequence_length):
            preds = model.predict([java_seq, python_in], verbose=0)
            next_token = tf.argmax(preds[0, i]).numpy()
            translated_tokens.append(next_token)
            if next_token == 0:
                break
            if i + 1 < sequence_length:
                python_in = tf.tensor_scatter_nd_update(
                    python_in, [[0, i + 1]], [next_token]
                )

        tokens = [index_to_word.get(t, "") for t in translated_tokens]
        return " ".join(tokens).replace("[UNK]", "").strip()

    except Exception as e:
        return f"Local Model Error: {str(e)}"

def translate_code(code_snippet, source_lang, target_lang):
    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}"

# --- Streamlit UI ---

st.title("🔄  Programming Language Translator")
st.write("Translate code between programming languages using 3-tier logic:")

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)

# State initialization
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
        attempt = st.session_state.translate_attempts

        with st.spinner(f"Translating..."):
            # First click
            if attempt == 1:
                if source_lang == "Java" and target_lang == "Python":
                    st.session_state.translated_code = translate_with_local_model(code_input)
                else:
                    st.session_state.translated_code = translate_code(code_input, source_lang, target_lang)
            else:
                # Second and later attempts -> Gemini
                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.")







# Best version. It doesn't having trained model only.

# 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("🔄 Code Translator with Gemini AI")
# 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 LLM

# 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")  # Fetch token securely
# # Change MODEL_ID to a better model
# # MODEL_ID = "Salesforce/codet5p-770m"  # CodeT5+ (Recommended)
# MODEL_ID = "bigcode/starcoder2-15b"  # StarCoder2
# # MODEL_ID = "meta-llama/CodeLlama-34b-Instruct"  # Code Llama

# # API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"

# 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.")