Update app.py

app.py

@@ -2,83 +2,43 @@ from huggingface_hub import InferenceClient
 import gradio as gr
 import random
 import pandas as pd
-from io import BytesIO
+from io import BytesIO 
 import csv
 import os
-import io
+import io 
 import tempfile
 import re
-import streamlit as st
-import torch
 from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
-import time
-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.")
 
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
-lang_id = {
-    "Afrikaans": "af",
-    "Amharic": "am",
-    "Arabic": "ar",
-    "Asturian": "ast",
-    "Azerbaijani": "az",
-    "Bashkir": "ba",
-    "Belarusian": "be",
-    "Bulgarian": "bg",
-    "Bengali": "bn",
-    "Breton": "br",
-    "Bosnian": "bs",
-    "Catalan": "ca",
-    "Cebuano": "ceb",
-    "Czech": "cs",
-    "Welsh": "cy",
-    "Danish": "da",
-    "German": "de",
-    "Greeek": "el",
-    "English": "en",
-    "Spanish": "es",
-    "Estonian": "et",
-    "Persian": "fa",
-    "Fulah": "ff",
-    "Finnish": "fi",
-    "French": "fr",
-    "Western Frisian": "fy",
-    "Irish": "ga",
-    "Gaelic": "gd",
-    "Galician": "gl",
-    "Gujarati": "gu",
-    "Hausa": "ha",
-    "Hebrew": "he",
-    "Hindi": "hi",
-    "Croatian": "hr",
-    "Haitian": "ht",
-    "Hungarian": "hu",
-    "Armenian": "hy",
-    "Indonesian": "id"
-}
-
-@st.cache(suppress_st_warning=True, allow_output_mutation=True)
-def load_model(pretrained_model: str = "facebook/m2m100_1.2B", 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
+tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B")
+model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B")
+
+def translate_to_english(text, source_lang):
+    encoded_input = tokenizer(text, return_tensors="pt")
+    generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id("en"))
+    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+    return translated_text
+
+def translate_to_azerbaijani(text):
+    encoded_input = tokenizer(text, return_tensors="pt")
+    generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id("az"))
+    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+    return translated_text
 
 def extract_text_from_excel(file):
     df = pd.read_excel(file)
     text = ' '.join(df['Unnamed: 1'].astype(str))
-    return text
+    source_lang = "az"  # Azerbaijani
+    english_text = translate_to_english(text, source_lang)
+    return english_text
 
 def save_to_csv(sentence, output, filename="synthetic_data.csv"):
+    azerbaijani_output = translate_to_azerbaijani(output)
     with open(filename, mode='a', newline='', encoding='utf-8') as file:
         writer = csv.writer(file)
-        writer.writerow([sentence, output])
+        writer.writerow([sentence, azerbaijani_output])
 
 def generate(file, temperature, max_new_tokens, top_p, repetition_penalty, num_similar_sentences):
     text = extract_text_from_excel(file)
@@ -88,7 +48,7 @@ def generate(file, temperature, max_new_tokens, top_p, repetition_penalty, num_s
     with tempfile.NamedTemporaryFile(mode='w', newline='', delete=False, suffix='.csv') as tmp:
         fieldnames = ['Original Sentence', 'Generated Sentence']
         writer = csv.DictWriter(tmp, fieldnames=fieldnames)
-        writer.writeheader()
+        writer.writeheader()  
 
         for sentence in sentences:
             sentence = sentence.strip()
@@ -117,28 +77,7 @@ def generate(file, temperature, max_new_tokens, top_p, repetition_penalty, num_s
                     if not generated_sentences:
                         break
                     generated_sentence = generated_sentences.pop(random.randrange(len(generated_sentences)))
-
-                    # Translate generated sentence to English
-                    tokenizer, model = load_model()
-                    src_lang = lang_id[language]
-                    trg_lang = lang_id["English"]
-                    tokenizer.src_lang = src_lang
-                    with torch.no_grad():
-                        encoded_input = tokenizer(generated_sentence, return_tensors="pt").to(device)
-                        generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang))
-                        translated_sentence = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-
-                    # Translate original sentence to Azerbaijani
-                    tokenizer, model = load_model()
-                    src_lang = lang_id["English"]
-                    trg_lang = lang_id["Azerbaijani"]
-                    tokenizer.src_lang = src_lang
-                    with torch.no_grad():
-                        encoded_input = tokenizer(sentence, return_tensors="pt").to(device)
-                        generated_tokens = model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang))
-                        translated_sentence_az = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-
-                    writer.writerow({'Original Sentence': translated_sentence_az, 'Generated Sentence': translated_sentence})
+                    writer.writerow({'Original Sentence': sentence, 'Generated Sentence': generated_sentence})
 
             except Exception as e:
                 print(f"Error generating data for sentence '{sentence}': {e}")
@@ -147,7 +86,7 @@ def generate(file, temperature, max_new_tokens, top_p, repetition_penalty, num_s
 
     return tmp_path
 
-gr.Interface(
+gr.Interface( 
     fn=generate,
     inputs=[
         gr.File(label="Upload Excel File", file_count="single", file_types=[".xlsx"]),
@@ -156,7 +95,6 @@ gr.Interface(
         gr.Slider(label="Top-p (nucleus sampling)", value=0.95, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
         gr.Slider(label="Repetition penalty", value=1.0, minimum=1.0, maximum=2.0, step=0.1, interactive=True, info="Penalize repeated tokens"),
         gr.Slider(label="Number of similar sentences", value=10, minimum=1, maximum=20, step=1, interactive=True, info="Number of similar sentences to generate for each original sentence"),
-        gr.Dropdown(label="Language of the input data", choices=list(lang_id.keys()), value="English")
     ],
     outputs=gr.File(label="Synthetic Data "),
     title="SDG",