Upload app.py

app.py

@@ -5,6 +5,8 @@
 # - https://huggingface.co/EnglishVoice/t5-base-keywords-to-headline?text=diabetic+diet+plan
 # - Apache 2.0
 
+# In[2]:
+
 
 import torch
 from transformers import T5ForConditionalGeneration,T5Tokenizer
@@ -15,32 +17,83 @@ model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-keyword
 tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-keywords-to-headline", clean_up_tokenization_spaces=True, legacy=False)
 model = model.to(device)
 
-keywords = "music, sleep, night"
-
-text =  "headline: " + keywords
-encoding = tokenizer.encode_plus(text, return_tensors = "pt")
-input_ids = encoding["input_ids"].to(device)
-attention_masks = encoding["attention_mask"].to(device)
-beam_outputs = model.generate(
-    input_ids = input_ids,
-    attention_mask = attention_masks,
-    max_new_tokens = 25,
-    do_sample = True,
-    num_return_sequences = 5,
-    temperature = 1.2,
-    #num_beams = 20,
-    #num_beam_groups = 20,
-    #diversity_penalty=0.8,
-    no_repeat_ngram_size = 3,
-    penalty_alpha = 0.8,
-    #early_stopping = True,
-    top_k = 15,
-    #top_p = 0.60,
-)
-
-for i in range(len(beam_outputs)):
-    result = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
-    print(result)
+
+
+# In[5]:
+
+
+def title_gen(keywords):
+
+    text =  "headline: " + keywords
+    encoding = tokenizer.encode_plus(text, return_tensors = "pt")
+    input_ids = encoding["input_ids"].to(device)
+    attention_masks = encoding["attention_mask"].to(device)
+    beam_outputs = model.generate(
+        input_ids = input_ids,
+        attention_mask = attention_masks,
+        max_new_tokens = 30,
+        do_sample = True,
+        num_return_sequences = 5,
+        temperature = 1.2,
+        #num_beams = 20,
+        #num_beam_groups = 20,
+        #diversity_penalty=0.8,
+        no_repeat_ngram_size = 3,
+        penalty_alpha = 0.8,
+        #early_stopping = True,
+        top_k = 15,
+        #top_p = 0.60,
+    )
+
+    titles = ""
+    
+    for i in range(len(beam_outputs)):
+        result = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
+        titles += f"{result}<br>" #Create string with titles and <br> tag for html reading in gradio html
+    
+    return titles
+
+
+# In[1]:
+
+
+import gradio as gr
+
+
+# In[ ]:
+
+
+iface = gr.Interface(fn=paraphrase, 
+                     inputs=[gr.Textbox(label="Paste 2 or more keywords searated by a comma.", lines=1), "checkbox", gr.Slider(0.1, 2, 0.8)],
+                     outputs=[gr.HTML(label="Titles:")],
+                     title="AI Keywords to Title Generator", 
+                     description="Turn keywords into creative suggestions",
+                     article="<div align=left><h1>AI Creative Title Generator</h1><li>With just keywords, generate a list of creative titles.</li><li>Click on Submit to generate more creative and diverse titles.</li><p>AI Model:<br><li>T5 Model trained on a dataset of titles and related keywords</li><li>Original model id: EnglishVoice/t5-base-keywords-to-headline by English Voice AI Labs</li></p><p>Default parameter details:<br><li>Temperature = 1.2, no_repeat_ngram_size=3, top_k = 15, penalty_alpha = 0.8, max_new_tokens = 30</li></div>",
+                     flagging_mode='never'
+                    )
+
+iface.launch()
+
+
+# In[ ]:
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+# In[ ]:
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+# In[ ]:
 
 
 '''
@@ -90,6 +143,21 @@ demo.launch()
 '''
 
 
+# In[164]:
+
+
+import gc
+gc.collect()
+
+
+# In[166]:
+
+
+gr.close_all()
+
+
+# In[ ]:
+