Update app.py

app.py

@@ -4,39 +4,50 @@ from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoMode
 import gradio as gr
 from gradio.mix import Parallel, Series
 #import torch.nn.functional as F
-
+from aitextgen import aitextgen
 from datasets import load_dataset
 dataset = load_dataset("bananabot/engMollywoodSummaries")
 dataset
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-model_name = "EleutherAI/gpt-neo-125M"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+ai = aitextgen(model="EleutherAI/gpt-neo-1.3B")
+
+#model_name = "EleutherAI/gpt-neo-125M"
+#tokenizer = AutoTokenizer.from_pretrained(model_name)
+#model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
 
-max_length=123
-input_txt = "This malayalam movie is about"
-n_steps = 8
+#max_length=123
+#input_txt = "This malayalam movie is about"
+#n_steps = 8
 
-input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device)
-output = model.generate(input_ids,  max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37, top_k=69, top_p=0.96)
-print(tokenizer.decode(output[0]))
+#input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device)
+#output = model.generate(input_ids,  max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37, top_k=69, top_p=0.96)
+#print(tokenizer.decode(output[0]))
 
-def generate(input_txt):
-    output = model.generate(input_ids,  max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37,           top_k=69, top_p=0.96)
-    print (output)
+#def generate(input_txt):
+#    output = model.generate(input_ids,  max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37,           top_k=69, top_p=0.96)
+#    print (output)
+
+def ai_text(inp):
+  generated_text = ai.generate_one(max_length=123, prompt = inp, no_repeat_ngram_size=3, num_beams=5, do_sample=True, temperature=1.37, top_k=69, top_p=0.96)
+  print(type(generated_text))
+  return generated_text
     
-inputs= gr.inputs.Textbox(lines=7, placeholder="Enter the beginning of your mollywood movie idea and the നിർമ്മിത ബുദ്ധി will fill in the rest...")
+#inputs= gr.inputs.Textbox(lines=7, placeholder="Enter the beginning of your mollywood movie idea and the നിർമ്മിത ബുദ്ധി will fill in the rest...")
 
 #integrate a working translator later!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
 #generator = output 
 #translator = gr.Interface.load("models/Helsinki-NLP/opus-mt-en-ml")
 #gr.Series(generator, translator, inputs=gr.inputs.Textbox(lines=7, label="Input Text")).launch()  # this demo generates text, then translates it to Malayalam, and outputs the final result.
 
-interface = gr.Interface(fn=generate, 
-                        inputs=inputs,
-                        outputs='text',
-                        title='AI based Mollywood movie idea generator')
+#interface = gr.Interface(fn=generate, 
+#                        inputs=inputs,
+#                        outputs='text',
+#                        title='AI based Mollywood movie idea generator')
+
+#interface.launch()
 
-interface.launch()
+output_text = gr.outputs.Textbox()
+gr.Interface(ai_text,"textbox", output_text, title="AI based Mollywood movie idea generator",
+             description="Enter the beginning of your malayalam movie idea and the നിർമ്മിത ബുദ്ധി will fill in the rest...").launch()