Update app.py

app.py

@@ -1,53 +1,12 @@
 import gradio as gr
-from transformers import pipeline
-description = """
-<p>
-<center>
-This bot was trained on a dataset of 1000 movie reviews from IMDB. It can suggest movies similar to the one you liked!
-<img src="https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot/resolve/main/img/rick.png" alt="rick" width="200"/>
-</center>
-</p>
-"""
-model = pipeline("text-generation", model="charoori/llm4movies")
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-base_model_id = "mistralai/Mistral-7B-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(
-    base_model_id,
-    add_bos_token=True,
-)
-model = AutoModelForCausalLM.from_pretrained("charoori/llm4movies")
-
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response 
-    # model_input = tokenizer(eval_prompt, return_tensors="pt")
-    history = model.generate(bot_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(model.generate(**bot_input_ids, max_new_tokens=256, repetition_penalty=1.15)[0], skip_special_tokens=True)
-
-    # response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    #print('decoded_response-->>'+str(response))
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    #print('response-->>'+str(response))
-    return response, history
-
-
-interface = gr.Interface(
-    fn=predict,
-	title = "Find your next movie!",
-    inputs="textbox",
-    outputs="text",
+from gradio import inputs
+description = "Story generation with GPT-2"
+interface = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator", 
+	title = "Story Generation with GPT-2",
+	inputs = [
+        gr.inputs.Textbox(lines=7, label="Story"),
+    ],
     description=description,
-    examples=[["I liked the movie Matrix because it was very interesting and had a great story. Suggest something similar"]]
+    examples=[["Adventurer is approached by a mysterious stranger in the tavern for a new quest"]]
 )
-interface.launch()
-
+interface.launch()