Added Gradio app

app.py

@@ -1,71 +1,10 @@
 
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-from transformers import AutoTokenizer
-from peft import PeftModel, PeftConfig
-
-config = PeftConfig.from_pretrained("TohidA/LlamaInstructMona")
-model = AutoModelForCausalLM.from_pretrained("mlabonne/llama-2-7b-miniguanaco")
-model = PeftModel.from_pretrained(model, "TohidA/LlamaInstructMona")
-
-if torch.cuda.is_available():
-    model = model.cuda()
-
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-
-def prompt(instruction, input=''):
-    if input=='':
-        return f"Below is an instruction that describes a task. Write a response that appropriately completes the request. \n\n### Instruction:\n{instruction} \n\n### Response:\n"
-    return f"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. \n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
-
-tokenizer.pad_token = tokenizer.eos_token
-tokenizer.pad_token_id = tokenizer.eos_token_id
-
-def instruct(instruction, input='', temperature=0.7, top_p=0.95, top_k=4, max_new_tokens=128, do_sample=False, penalty_alpha=0.6, repetition_penalty=1., stop="\n\n"):
-    input_ids = tokenizer(prompt(instruction, input).strip(), return_tensors='pt').input_ids.cuda()
-    with torch.cuda.amp.autocast():
-        outputs = model.generate(
-            input_ids=input_ids,
-            return_dict_in_generate=True,
-            output_scores=True,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-            do_sample=do_sample,
-            repetition_penalty=repetition_penalty
-        )
-    if stop=="":
-        return tokenizer.decode(outputs.sequences[0], skip_special_tokens=True).split("### Response:")[1].strip(), prompt(instruction, input)
-    return tokenizer.decode(outputs.sequences[0], skip_special_tokens=True).split("### Response:")[1].strip().split(stop)[0].strip(), prompt(instruction, input)
-    
-
-import locale
-locale.getpreferredencoding = lambda: "UTF-8"
-
 import gradio as gr
 
-input_text = gr.Textbox(label="Input")
-instruction_text = gr.Textbox(label="Instruction")
-temperature = gr.Slider(label="Temperature", minimum=0, maximum=1, value=0.7, step=0.05)
-top_p = gr.Slider(label="Top-P", minimum=0, maximum=1, value=0.95, step=0.01)
-top_k = gr.Slider(label="Top-K", minimum=0, maximum=128, value=40, step=1)
-max_new_tokens = gr.Slider(label="Tokens", minimum=1, maximum=256, value=64)
-do_sample = gr.Checkbox(label="Do Sample", value=True)
-penalty_alpha = gr.Slider(minimum=0, maximum=1, value=0.5)
-repetition_penalty = gr.Slider(minimum=1., maximum=2., value=1., step=0.1)
-stop = gr.Textbox(label="Stopping Criteria", value="")
+def greet(name):
+    return "Hello " + name + "!!"
 
-output_prompt = gr.Textbox(label="Prompt")
-output_text = gr.Textbox(label="Output")
-description = """
-The [TohidA/InstructLlamaMONA-withMONAdataset](https://hf.co/TohidA/LlamaInstructMona). A Llama chat 7B model finetuned on an [instruction dataset](https://huggingface.co/mlabonne/llama-2-7b-miniguanaco), then finetuned with the RL/PPO using a [Reward model](https://huggingface.co/TohidA/MONAreward) which is a BERT classifier trained on [Monda dataset](https://huggingface.co/datasets/TohidA/MONA), with [low rank adaptation](https://arxiv.org/abs/2106.09685) for a single epoch.
-"""
-gr.Interface(fn=instruct,
-             inputs=[instruction_text, input_text, temperature, top_p, top_k, max_new_tokens, do_sample, penalty_alpha, repetition_penalty, stop],
-             outputs=[output_text, output_prompt],
-             title="InstructLlamaMONA 7B Gradio Demo", description=description).launch(
-    debug=True,
-    share=True
-)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+    
+if __name__ == "__main__":
+    iface.launch()