Create app.py

app.py

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+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import gradio as gr
+
+# Load fine-tuned T5 models for different tasks
+translation_model_en_bn = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_nmt_en_bn")
+translation_tokenizer_en_bn = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_nmt_en_bn")
+
+translation_model_bn_en = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_nmt_bn_en")
+translation_tokenizer_bn_en = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_nmt_bn_en")
+
+summarization_model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
+summarization_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
+
+paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_banglaparaphrase")
+paraphrase_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_banglaparaphrase")
+
+# Function to perform machine translation
+def translate_text(input_text):
+    inputs = translation_tokenizer_en_bn("translate: " + input_text, return_tensors="pt")
+    outputs = translation_model_en_bn.generate(**inputs)
+    translated_text = translation_tokenizer_en_bn.decode(outputs[0], skip_special_tokens=True)
+    return translated_text
+
+# Function to perform summarization
+def summarize_text(input_text):
+    inputs = summarization_tokenizer("summarize: " + input_text, return_tensors="pt")
+    outputs = summarization_model.generate(**inputs)
+    summarized_text = summarization_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summarized_text
+
+# Function to perform paraphrasing
+def paraphrase_text(input_text):
+    inputs = paraphrase_tokenizer("paraphrase: " + input_text, return_tensors="pt")
+    outputs = paraphrase_model.generate(**inputs)
+    paraphrased_text = paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return paraphrased_text
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=translate_text,  # Placeholder function; will be updated dynamically based on task selection
+    inputs=gr.Textbox("textarea", label="Input Text"),
+    outputs=gr.Textbox("auto", label="Output Text"),
+    live=True
+)
+
+# Function to update the Gradio interface based on task selection
+def update_interface(change):
+    selected_task = task_selector.value
+
+    if selected_task == 'Translate':
+        iface.fn = translate_text
+    elif selected_task == 'Summarize':
+        iface.fn = summarize_text
+    elif selected_task == 'Paraphrase':
+        iface.fn = paraphrase_text
+
+# Dropdown widget to select the task
+task_selector = gr.Dropdown(
+    ["Translate", "Summarize", "Paraphrase"],
+    default="Translate",
+    label="Select Task"
+)
+
+# Attach the update function to the dropdown widget
+task_selector.observe(update_interface, names='value')
+
+# Launch the Gradio app
+iface.launch()