Create app.py

app.py

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+import gradio as gr
+import torch
+import transformers as trf
+
+# Load the summarization model
+summarization_model_path = 'ieuniversity/News-Translator'
+summarization_tokenizer = trf.AutoTokenizer.from_pretrained(summarization_model_path)
+summarization_model = trf.AutoModelForSeq2SeqLM.from_pretrained(summarization_model_path)
+
+# Load the translation model
+translation_model_path = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua'
+translation_tokenizer = trf.AutoTokenizer.from_pretrained(translation_model_path)
+translation_model = trf.AutoModelForSeq2SeqLM.from_pretrained(translation_model_path)
+
+def summarize_and_translate(news_text):
+    # Summarize the news article
+    max_input_length = 512
+    max_output_length = 128
+    input_encoded = summarization_tokenizer.encode_plus(news_text, add_special_tokens=True,
+                                                         max_length=max_input_length, pad_to_max_length=True,
+                                                         return_attention_mask=True, return_tensors='pt')
+    input_ids = input_encoded['input_ids']
+    attention_mask = input_encoded['attention_mask']
+    output_ids = summarization_model.generate(input_ids=input_ids, attention_mask=attention_mask,
+                                               max_length=max_output_length)
+    summary_text = summarization_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    # Translate the summary to Quechua
+    input_encoded = translation_tokenizer(summary_text, padding=True, truncation=True, max_length=512, return_tensors='pt')
+    input_ids = input_encoded['input_ids']
+    attention_mask = input_encoded['attention_mask']
+    output_ids = translation_model.generate(input_ids=input_ids, attention_mask=attention_mask,
+                                             max_length=512)
+    output_text = translation_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    
+    return output_text
+
+# Define the input and output interfaces for Gradio
+input_interface = gr.inputs.Textbox(label="Input News Text (Spanish)")
+output_interface = gr.outputs.Textbox(label="Output News Text (Quechua)")
+
+# Create and launch the Gradio app
+
+iface = gr.Interface(fn=summarize_and_translate, inputs=input_interface, outputs=output_interface, title="Spanish-to-Quechua News Summarization and Translation")
+iface.launch()