Update app.py

app.py

@@ -1,71 +1,12 @@
-import streamlit as st
-import os
-import io
-from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
-import time
-import json
-from typing import List
-import torch
-import random
-import logging
-
-if torch.cuda.is_available():
-    device = torch.device("cuda:0")
-else:
-    device = torch.device("cpu")
-    logging.warning("GPU not found, using CPU, translation will be very slow.")
-
-st.cache(suppress_st_warning=True, allow_output_mutation=True)
-st.set_page_config(page_title="M2M100 Translator")
-
-lang_id = {
-    "English": "en",
-    "French": "fr",
-}
-
-
-@st.cache(suppress_st_warning=True, allow_output_mutation=True)
-def load_model(
-    pretrained_model: str = "facebook/m2m100_418M",
-    cache_dir: str = "models/",
-):
-    tokenizer = M2M100Tokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir)
-    model = M2M100ForConditionalGeneration.from_pretrained(
-        pretrained_model, cache_dir=cache_dir
-    ).to(device)
-    model.eval()
-    return tokenizer, model
-
-
-st.title("M2M100 Translator")
-
-
-user_input: str = st.text_area(
-    "Input text",
-    height=200,
-    max_chars=5120,
-)
-
-source_lang = st.selectbox(label="Source language", options=list(lang_id.keys()))
-target_lang = st.selectbox(label="Target language", options=list(lang_id.keys()))
-
-if st.button("Run"):
-    time_start = time.time()
-    tokenizer, model = load_model()
-
-    src_lang = lang_id[source_lang]
-    trg_lang = lang_id[target_lang]
-    tokenizer.src_lang = src_lang
-    with torch.no_grad():
-        encoded_input = tokenizer(user_input, return_tensors="pt").to(device)
-        generated_tokens = model.generate(
-            **encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang)
-        )
-        translated_text = tokenizer.batch_decode(
-            generated_tokens, skip_special_tokens=True
-        )[0]
-
-    time_end = time.time()
-    st.success(translated_text)
-
-    st.write(f"Computation time: {round((time_end-time_start),3)} segs")
+from transformers import pipeline
+import gradio as gr
+pipe= pipeline('text2text-generation', model="facebook/m2m100_418M")
+def generate_text(inp):
+    output=pipe(inp, forced_bos_token_id=pipe.tokenizer.get_lang_id('en'))
+    tln=output[0]
+    for item in tln:
+        result=tln[item]
+    return result
+#Gradio Interface
+output_text = gr.outputs.Textbox()
+gr.Interface(generate_text,"textbox", output_text).launch(inline=False)