Delete app.py

app.py

@@ -1,59 +0,0 @@
-import gradio as gr
-import torch
-
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-
-# Define hyperparameters
-max_seq_length = 512
-max_output_length = 1024
-num_beams = 16
-length_penalty = 1.4
-no_repeat_ngram_size = 2
-temperature = 0.7
-top_k = 150
-top_p = 0.92
-repetition_penalty = 2.1
-early_stopping = True
-
-# Load the pre-trained model and tokenizer
-model_name = "google/flan-t5-large"
-tokenizer = T5Tokenizer.from_pretrained(model_name, model_max_length=512)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-if torch.cuda.device_count() > 1:
-    device_ids = [i for i in range(torch.cuda.device_count())]
-    model = torch.nn.DataParallel(T5ForConditionalGeneration.from_pretrained(model_name, return_dict=True), device_ids=device_ids)
-else:
-    model = T5ForConditionalGeneration.from_pretrained(model_name, return_dict=True)
-
-model.to(device)
-
-# Define a function to generate a response to user input
-def chatbot(text):
-    with torch.no_grad():
-        # Tokenize the input text and convert to a PyTorch tensor
-        input_ids = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=max_seq_length).input_ids.to(device)
-
-        # Generate a response using the model
-        if torch.cuda.device_count() > 1:
-            outputs = model.module.generate(input_ids, min_length=max_seq_length, max_new_tokens=max_output_length, num_beams=num_beams, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, early_stopping=early_stopping)
-        else:
-            outputs = model.generate(input_ids, min_length=max_seq_length, max_new_tokens=max_output_length, num_beams=num_beams, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, early_stopping=early_stopping)
-
-        # Decode the response and return it
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return response
-
-chat_help_text = "Welcome! This ChatBot is designed to answer questions about a wide range of topics. " \
-                 "Please note that the ChatBot may not always provide accurate or complete answers, and may not " \
-                 "understand certain questions. To use the ChatBot, simply type in your question in the text box " \
-                 "below and hit Enter or click the button. Please keep in mind that the ChatBot is not perfect " \
-                 "and may provide inaccurate or incomplete answers. It is best suited for simple factual " \
-                 "questions rather than complex or nuanced inquiries."
-
-# Create a Gradio interface
-iface = gr.Interface(fn=chatbot, inputs="text", outputs="text", title="NuNet Inferencing Demo",
-                     description=chat_help_text)
-
-iface.launch(share=True)
-