Update app.py

app.py

@@ -1,43 +1,19 @@
-pip -q install langchain huggingface_hub transformers sentence_transformers accelerate bitsandbytes
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
 
-import os
-os.environ['HUGGINGFACEHUB_API_TOKEN'] = prompttoken
+tokenizer = AutoTokenizer.from_pretrained("merve/chatgpt-prompts-bart-long")
+model = AutoModelForSeq2SeqLM.from_pretrained("merve/chatgpt-prompts-bart-long", from_tf=True)
 
-from langchain import PromptTemplate, HuggingFaceHub, LLMChain
+def generate(prompt):
 
-template = """Question: {question}
-Answer: Let's think step by step."""
+    batch = tokenizer(prompt, return_tensors="pt")
+    generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
+    output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    return output[0]
 
-prompt = PromptTemplate(template=template, input_variables=["question"])
+input_component = gr.Textbox(label = "Input a persona, e.g. photographer", value = "photographer")
+output_component = gr.Textbox(label = "Prompt")
+examples = [["photographer"], ["developer"]]
+description = "This app generates ChatGPT prompts, it's based on a BART model trained on [this dataset](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts). 📓 Simply enter a persona that you want the prompt to be generated based on. 🧙🏻🧑🏻‍🚀🧑🏻‍🎨🧑🏻‍🔬🧑🏻‍💻🧑🏼‍🏫🧑🏽‍🌾"
+gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "👨🏻‍🎤 ChatGPT Prompt Generator 👨🏻‍🎤", description=description).launch()
 
-llm_chain = LLMChain(prompt=prompt, 
-                     llm=HuggingFaceHub(repo_id="google/flan-t5-xl", 
-                                        model_kwargs={"temperature":0, 
-                                                      "max_length":64}))
-
-
-from langchain.llms import HuggingFacePipeline
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoModelForSeq2SeqLM
-
-model_id = 'Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_id, from_tf=True)
-
-pipeline = pipeline(
-    "text2text-generation",
-    model=model, 
-    tokenizer=tokenizer, 
-    max_length=128
-)
-
-local_llm = HuggingFacePipeline(pipeline=pipeline)
-
-
-llm_chain = LLMChain(prompt=prompt, 
-                     llm=local_llm
-                     )
-
-question = "Excel Sheet"
-
-print(llm_chain.run(question))