Update app.py

app.py

@@ -1,29 +1,39 @@
 import gradio as gr
 from gradio import mix
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 title = "Miniature"
 description = "Gradio Demo for a miniature with GPT. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
 
 
-examples = [
-    ['A kid is playing with']
-]
 
-io = gr.Interface.load("huggingface/keras-io/text-generation")
+tokenizer = AutoTokenizer.from_pretrained("aditi2222/automatic_title_generation")
 
+model = AutoModelForSeq2SeqLM.from_pretrained("aditi2222/automatic_title_generation")
+  
 
+def tokenize_data(text):
+    # Tokenize the review body
+    input_ =  str(text) + ' </s>'
+    max_len = 120
+    # tokenize inputs
+    tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
 
-def inference(text):
+    inputs={"input_ids": tokenized_inputs['input_ids'],
+        "attention_mask": tokenized_inputs['attention_mask']}
+    return inputs
 
-    return generator(text)
-    
-     
+def generate_answers(text):
+    inputs = tokenize_data(text)
+    results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
+                            max_length=120,
+                            top_k=120,
+                            top_p=0.98,
+                            early_stopping=True,
+                            num_return_sequences=1)
+    answer = tokenizer.decode(results[0], skip_special_tokens=True)
+    return answer
 
-gr.Interface(
-    inference, 
-    [gr.inputs.Textbox(label="Input")], 
-    gr.outputs.Textbox(label="Output"),
-    examples=examples,
-    # article=article,
-    title=title,
-    description=description).launch(enable_queue=True, cache_examples=True)
+iface = gr.Interface(fn=generate_answers, inputs=['text'], outputs=["text"])
+iface.launch(inline=False, share=True)