Update app.py

app.py

@@ -1,26 +1,53 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import requests
+import os
 
-# Load CodeT5 model from Hugging Face
-MODEL_NAME = "Salesforce/codet5-large"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+# Ensure the Hugging Face API Token is available
+API_TOKEN = os.getenv("HF_API_TOKEN")
+if not API_TOKEN:
+    st.error("⚠️ API Token is missing! Please set HF_API_TOKEN as an environment variable.")
+    st.stop()
 
-def translate_code(code_snippet, source_lang, target_lang):
-    """
-    Translate code using CodeT5 model.
-    """
-    prompt = f"""Translate this {source_lang} code to {target_lang}:
+# Use Code Llama for better translation
+MODEL_ID = "codellama/CodeLlama-7b-Instruct-hf"
+API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
+HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
 
-{code_snippet}"""
+def translate_code(code_snippet, source_lang, target_lang):
+    """Translate code between languages using Hugging Face API."""
+    prompt = f"""### Task: Convert {source_lang} code to {target_lang}.
     
-    # Tokenize and generate translation
-    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
-    outputs = model.generate(**inputs, max_length=512)
+    #### {source_lang} Code:
+    ```{source_lang.lower()}
+    {code_snippet}
+    ```
     
-    # Decode the output
-    translated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translated_code
+    #### Translated {target_lang} Code:
+    """
+
+    try:
+        response = requests.post(API_URL, headers=HEADERS, json={"inputs": prompt})
+        
+        if response.status_code == 200:
+            result = response.json()
+            if isinstance(result, list) and result:
+                generated_text = result[0].get("generated_text", "")
+                translated_code = generated_text.split(f"#### Translated {target_lang} Code:")[-1].strip()
+                return translated_code if translated_code else "⚠️ No translated code received."
+            else:
+                return "⚠️ Unexpected API response format."
+        elif response.status_code == 400:
+            return "⚠️ Error: Bad request. Check your input."
+        elif response.status_code == 401:
+            return "⚠️ Error: Unauthorized. Check your API token."
+        elif response.status_code == 403:
+            return "⚠️ Error: Access Forbidden. You may need special model access."
+        elif response.status_code == 503:
+            return "⚠️ Error: Model is loading. Please wait and try again."
+        else:
+            return f"⚠️ API Error {response.status_code}: {response.text}"
+    except requests.exceptions.RequestException as e:
+        return f"⚠️ Network Error: {str(e)}"
 
 # Streamlit UI
 st.title("🔄 AI Code Translator")
@@ -40,4 +67,4 @@ if st.button("Translate"):
         with st.spinner("Translating... ⏳"):
             translated_code = translate_code(code_input, source_lang, target_lang)
             st.subheader(f"Translated {target_lang} Code:")
-            st.code(translated_code, language=target_lang.lower())
+            st.code(translated_code, language=target_lang.lower())