Update app.py

app.py

@@ -1,35 +1,13 @@
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-# Load DialoGPT model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-
-# Function to generate a response
-def generate_response(chat_history_ids, new_user_input):
-    new_user_input_ids = tokenizer.encode(new_user_input + tokenizer.eos_token, return_tensors='pt')
-    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if chat_history_ids is not None else new_user_input_ids
-    return model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+# prompt: write a streamlit app that converts english text into french text when translate button is pressed. The title of page should be "Translate to French"."
 
-# Streamlit app
-st.title("DialoGPT Chat")
-
-# Initialize chat history using st.session_state
-if 'chat_history_ids' not in st.session_state:
-    st.session_state.chat_history_ids = None
-
-# User input text box
-user_input = st.text_input("You:", "")
+import streamlit as st
+from transformers import pipeline
 
-# Check if the user pressed Enter
-if st.button("Send"):
-    if user_input:
-        # Generate response using st.session_state
-        st.session_state.chat_history_ids = generate_response(st.session_state.chat_history_ids, user_input)
+st.title("Translate to French")
 
-        # Display DialoGPT response
-        st.text("DialoGPT: {}".format(tokenizer.decode(st.session_state.chat_history_ids[:, -1][0], skip_special_tokens=True)))
+input_text = st.text_input("Enter text to translate")
 
-# Inform the user that the conversation has ended
-st.text("Press 'Send' to continue the conversation.")
+if st.button("Translate"):
+    pipe = pipeline(model="facebook/mbart-large-cc25")
+    translation = pipe(input_text, target_lang="fr")[0]["translation_text"]
+    st.write(translation)