Update app.py

app.py

@@ -1,22 +1,9 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 import os
-from huggingface_hub import login
-
-# Authenticate with Hugging Face using the environment variable
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-login(token=huggingface_token)
-
-# Load the tokenizer and model from Hugging Face
-@st.cache_resource
-def load_model():
-    model_name = "meta-llama/Meta-Llama-3.1-70B-Instruct"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
-    return tokenizer, model
+import openai
+import streamlit as st
 
-tokenizer, model = load_model()
+# Set up OpenAI API key
+openai.api_key = os.getenv("OPENAI_KEY")
 
 # Supported languages
 languages = ['English', 'French', 'Spanish', 'Hindi', 'Punjabi']
@@ -45,14 +32,22 @@ def main():
             st.success("Translation:")
             st.write(translation)
 
-# Function to translate text using the LLaMA model
+ Function to translate text using GPT-3.5
 def translate_text(text, input_language, output_language):
-    prompt = f"Translate the following from {input_language} to {output_language}:\n\n{text}"
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-    with torch.no_grad():
-        outputs = model.generate(**inputs, max_new_tokens=200)
-    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translation
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": f"You translate text from {input_language} to {output_language}"},
+                {"role": "user", "content": text}
+            ],
+            max_tokens=5000
+        )
+        translation = response.choices[0].message['content'].strip()
+        return translation
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 if __name__ == "__main__":
     main()