Update app.py

app.py

@@ -1,5 +1,14 @@
 import streamlit as st
 from transformers import pipeline
+
+import requests
+
+def query(payload, model_id, api_token):
+	headers = {"Authorization": f"Bearer {api_token}"}
+	API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+
 st.header("Ways to Improve Your Conversational Agents using 🤗 Hugging Face")
 
 st.write("There are many ways to improve your conversational agents using language models. In this blog post, I will walk you through a couple of tricks that will improve your conversational agent. 👾") 
@@ -26,10 +35,14 @@ st.image("./Translation.png")
 st.write("Your English intent classification model will be between these two models, your German to English model will translate the input to English and the output will go through the intent classification model, which will classify intent and select appropriate response (which is currently in English). The response will be translated back to German, which you can do in advance and do proofreading with a native speaker or directly pass it to a from English to German language model. For this use case, I highly recommend specific translation models instead of using sequence-to-sequence multilingual models like T5. ")
 
 
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
+
+
+model_id = "Helsinki-NLP/opus-mt-en-fr"
+
+#translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
 default_value_tr = "How are you?"
 input = st.text_input("Input in English", default_value_tr, key = "translation")
-outputs = translator(input)
+outputs = query(input, model_id, api_token)
 st.write("Translated Example:")
 st.write(outputs[0]["translation_text"])
 st.write("You can check out this [link](https://huggingface.co/models?pipeline_tag=translation&sort=downloads&search=helsinki-nlp) for available translation models.")