Update app.py

app.py

@@ -28,22 +28,14 @@ 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)
 
-# Fake adaptation to initialize vectorizers
+# 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"]))
 
-# Reverse lookup for Python vocab
 python_vocab = python_vectorizer.get_vocabulary()
 index_to_word = dict(enumerate(python_vocab))
 
-def decode_sequence(pred):
-    """Greedy decoding of the prediction."""
-    pred_ids = tf.argmax(pred, axis=-1).numpy()[0]
-    tokens = [index_to_word.get(i, "") for i in pred_ids]
-    code = " ".join(tokens).replace("[UNK]", "").strip()
-    return code
-
-# --- Translation Functions ---
+# --- 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}:
@@ -61,10 +53,9 @@ def fallback_translate_with_gemini(code_snippet, source_lang, target_lang):
         return f"Gemini API Error: {str(e)}"
 
 def translate_with_local_model(code_snippet):
-    """Local seq2seq Java→Python translation."""
     try:
         java_seq = java_vectorizer(tf.constant([code_snippet]))
-        python_in = tf.constant([[1] + [0] * (sequence_length - 1)])  # <start> token
+        python_in = tf.constant([[1] + [0] * (sequence_length - 1)])
         translated_tokens = []
 
         for i in range(sequence_length):
@@ -85,7 +76,6 @@ def translate_with_local_model(code_snippet):
         return f"Local Model Error: {str(e)}"
 
 def translate_code(code_snippet, source_lang, target_lang):
-    """Hugging Face translation."""
     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,
@@ -102,13 +92,14 @@ def translate_code(code_snippet, source_lang, target_lang):
 # --- Streamlit UI ---
 
 st.title("🔄  Programming Language Translator")
-st.write("Translate code between programming languages using 3-tier AI fallback.")
+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 = ""
@@ -119,12 +110,15 @@ if st.button("Translate"):
         attempt = st.session_state.translate_attempts
 
         with st.spinner(f"Translating..."):
+            # First click
             if attempt == 1:
-                st.session_state.translated_code = fallback_translate_with_gemini(code_input, source_lang, target_lang)
-            elif attempt == 2 and source_lang == "Java" and target_lang == "Python":
-                st.session_state.translated_code = translate_with_local_model(code_input)
+                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:
-                st.session_state.translated_code = translate_code(code_input, source_lang, target_lang)
+                # 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())
@@ -143,7 +137,6 @@ if st.button("Translate"):
 
 
 
-
 # version1: Without Trained model.
 
 # import streamlit as st