Update app.py

app.py

@@ -1,48 +1,46 @@
-import gradio as gr
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from peft import PeftModel
-import torch
-from googletrans import Translator
-
-# Load tokenizer and base model
-tokenizer = T5Tokenizer.from_pretrained("./")
-base_model = T5ForConditionalGeneration.from_pretrained("t5-small")
-model = PeftModel.from_pretrained(base_model, "./")
-
-# Use GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-translator = Translator()
-
-def summarize(text, language='en'):
-    if language != 'en':
-        text = translator.translate(text, dest='en').text
-
-    input_ids = tokenizer("summarize: " + text, return_tensors="pt", truncation=True, max_length=512).input_ids.to(device)
-    output_ids = model.generate(
-        input_ids,
-        max_length=80,
-        min_length=15,
-        length_penalty=1.5,
-        num_beams=8,
-        no_repeat_ngram_size=3,
-        early_stopping=True
-    )
-    summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-    if language != 'en':
-        summary = translator.translate(summary, dest=language).text
-
-    return summary
-
-gr.Interface(
-    fn=summarize,
-    inputs=[
-        gr.Textbox(lines=10, label="Enter Article"),
-        gr.Dropdown(choices=["en", "hi", "te", "fr", "es", "de"], value="en", label="Output Language")
-    ],
-    outputs="textbox",
-    title="T5 Summarizer (LoRA Optimized)",
-    description="Summarize long text using your fine-tuned T5-small model with LoRA adapters. Supports translation."
-).launch()
+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from peft import PeftModel
+from deep_translator import GoogleTranslator
+import torch
+
+# Load tokenizer and base model
+tokenizer = T5Tokenizer.from_pretrained("./")
+base_model = T5ForConditionalGeneration.from_pretrained("t5-small")
+model = PeftModel.from_pretrained(base_model, "./")
+
+# Use GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def summarize(text, language='en'):
+    if language != 'en':
+        text = GoogleTranslator(source='auto', target='en').translate(text)
+
+    input_ids = tokenizer("summarize: " + text, return_tensors="pt", truncation=True, max_length=512).input_ids.to(device)
+    output_ids = model.generate(
+        input_ids,
+        max_length=80,
+        min_length=15,
+        length_penalty=1.5,
+        num_beams=8,
+        no_repeat_ngram_size=3,
+        early_stopping=True
+    )
+    summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    if language != 'en':
+        summary = GoogleTranslator(source='en', target=language).translate(summary)
+
+    return summary
+
+gr.Interface(
+    fn=summarize,
+    inputs=[
+        gr.Textbox(lines=10, label="Enter Article"),
+        gr.Dropdown(choices=["en", "hi", "te", "fr", "es", "de"], value="en", label="Output Language")
+    ],
+    outputs="textbox",
+    title="T5 Summarizer (LoRA Optimized)",
+    description="Summarize articles using a fine-tuned T5 model with LoRA. Supports multiple languages."
+).launch()