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.")