Update Space

app.py

@@ -1,11 +1,19 @@
 import gradio as gr
-import gradio as gr
-from transformers import pipeline
-pipe = pipeline("text2text-generation", model="gowtham58/T_TL")
+import requests
+import os
+TK = os.environ['HF_TOKEN']
+API_URL = "https://api-inference.huggingface.co/models/gowtham58/T_TL"
+headers = {"Authorization": f"Bearer {TK}"}
 
-def get_tamil(text):
-    tamil_txt = pipe(text)
-    return tamil_txt[0]['generated_text']
+def get_output(text):
+	response = requests.post(API_URL, headers=headers, json={"inputs": text,})
+	response = response.json()
+	#print(response)
+	while response[0]=='error':
+		response = requests.post(API_URL, headers=headers, json={"inputs": text,})
+		response = response.json()
+	
+	return response[0]['generated_text']
 
 description = """TRANSLITERATE is to represent or spell in the characters of another alphabet. Normally we create tamil words using English Characters
 in our daily text conversations. This Model can generate the words in tamil given a transliterated tamil word in english"""
@@ -13,26 +21,26 @@ css = """
 h1 {
     text-align: center;
     display:block;
-    font-size:50px
 }
 p {
     text-align: center;
     display:block;
-    font-size:15px
+}
+.contain {
+  max-width: 900px;
+  margin: auto;
+  padding-top: 1.5rem;
 }
 
 """
 
 gr.Interface(
-    fn=get_tamil,
+    fn=get_output,
     inputs="textbox",
     outputs="text",
     title="Tamil Transliteraion",
     description=description,
-    examples=[["Vanakkam da Maaplaigala"], ["Naa Ready dha varava"]],
+    examples=[["Hello, Nanba epdi iruka"], ["Naa Ready dha varava"]],
     css = css, 
     allow_flagging="never",
 ).launch()
-
-#demo.launch()
-#gr.load("models/gowtham58/T_TL", alias="Tamil Transliteration").launch()