Eerste gradio single model interface werkt

README.md

@@ -1,5 +1,5 @@
 ---
-title: Radio Interface
+title: Gradio Interface
 emoji: 🏆
 colorFrom: purple
 colorTo: green

app.py

@@ -1,25 +1,39 @@
 import numpy as np
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
 def flip_text(x):
     return x[::-1]
  
 def flip_image(x):
     return np.fliplr(x)
+
+tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
+
+def generate_summary(text):
+    print(text)
+    inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
+    summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
  
-with gr.Blocks() as demo:
+with gr.Blocks() as main:
     gr.Markdown("My AI interface")
     with gr.Tab("Single models"):
-        text_input = gr.Textbox()
-        text_output = gr.Textbox()
-        text_button = gr.Button("Flip")
+        text_to_summarize = gr.Textbox(label="Text to summarize")
+        summary_output = gr.Textbox(label="Summary")
+        summarize_btn = gr.Button("Summarize")
+
+
     with gr.Tab("Multi models"):
         with gr.Row():
             image_input = gr.Image()
             image_output = gr.Image()
         image_button = gr.Button("Flip")
  
-    text_button.click(flip_text, inputs=text_input, outputs=text_output)
+    # text_button.click(flip_text, inputs=text_input, outputs=text_output)
     image_button.click(flip_image, inputs=image_input, outputs=image_output)
+    summarize_btn.click(generate_summary, inputs=text_to_summarize, outputs=summary_output)
  
-demo.launch()
+main.launch()