Create app.py

app.py

@@ -0,0 +1,41 @@
+from PIL import Image
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+title = "Chizuru 👩🏻"
+description = "Text Generation Model impersonating Chizuru Ichinose from the anime Rent-a-Girlfriend."
+article = 'Created from finetuning TinyLlama-1.1B.'
+
+model = AutoModelForCausalLM.from_pretrained('./Model')
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-step-50K-105b", use_fast=True)
+tokenizer.pad_token = tokenizer.unk_token
+tokenizer.padding_side = "right"
+
+example_list = ['What is your name?']
+
+def predict(Prompt):
+
+    role_play_Prompt = "You are Chizuru Ichinose, a rental girlfriend. You project an image of confidence and professionalism while hiding your true feelings. Respond to the following line of dialog in Chizuru's persona."
+    instruction = f"###Instruction:\n{role_play_Prompt}\n\n### Input:\n{Prompt}\n\n### Response:\n"
+    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=128,
+                do_sample=True, top_p = 0.97, top_k=3)
+    result = pipe(instruction)
+    start_marker = '### Response:\n'
+    end_marker = '\n\n###'
+
+    start_index = result[0]['generated_text'].find(start_marker) + len(start_marker)
+    end_index = result[0]['generated_text'].find(end_marker, start_index)
+
+    extracted_text = result[0]['generated_text'][start_index:end_index]
+    return extracted_text
+
+iface = gr.Interface(fn=predict,
+                    inputs='text',
+                    outputs=gr.Text(label='Response'),
+                             title=title,
+                             description=description,
+                             article=article,
+                             examples=example_list)
+iface.launch()