Update app.py

app.py

@@ -1,13 +1,82 @@
-import gradio as gr
-
-
-title = "Buckland Visions"
-description = "Buckland"
-article = "<p style = 'text-align: center' > Buckland</p>"
-examples = [
-    ["The candles"],
-    ["Love"],
-    ["The occult"]
-]
-gr.Interface.load("hgw3lss/gpt-j-6B-Buckland", inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
-                  title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
+import streamlit as st
+import numpy as np
+import pandas as pd
+import os
+import torch
+import torch.nn as nn
+from transformers.activations import get_activation
+from transformers import AutoTokenizer, AutoModelWithLMHead, AutoModelForCausalLM
+
+st.title('GPT2: To see all prompt outlines: https://huggingface.co/BigSalmon/InformalToFormalLincoln46')
+#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+number_of_outputs = st.sidebar.slider("Number of Outputs", 50, 350)
+
+@st.cache(allow_output_mutation=True)
+def get_model():
+    #model = #AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln6")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln5")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln4")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln2")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln24")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln25")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln26")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln27")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln28")
+    #model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln29")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln30")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln31")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln32")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln33")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln34")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln39")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln40")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln44")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln46")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln52")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points4")
+
+
+    tokenizer = AutoTokenizer.from_pretrained("hgw3lss/gpt-j-6B-Buckland")
+    model = AutoModelForCausalLM.from_pretrained("hgw3lss/gpt-j-6B-Buckland")
+    return model, tokenizer
+    
+model, tokenizer = get_model()
+g = """informal english: garage band has made people who know nothing about music good at creating music.
+Translated into the Style of Abraham Lincoln: garage band ( offers the uninitiated in music the ability to produce professional-quality compositions / catapults those for whom music is an uncharted art the ability the realize masterpieces / stimulates music novice's competency to yield sublime arrangements / begets individuals of rudimentary musical talent the proficiency to fashion elaborate suites ).
+informal english: chrome extensions can make doing regular tasks much easier to get done.
+***
+Translated into the Style of Abraham Lincoln: chrome extensions ( yield the boon of time-saving convenience / ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks / turbocharges the velocity with which one can conduct their obligations ).
+informal english: broadband is finally expanding to rural areas, a great development that will thrust them into modern life.
+Translated into the Style of Abraham Lincoln: broadband is ( ( finally / at last / after years of delay ) arriving in remote locations / springing to life in far-flung outposts / inching into even the most backwater corners of the nation ) that will leap-frog them into the twenty-first century.
+***
+informal english: google translate has made talking to people who do not share your language easier.
+Translated into the Style of Abraham Lincoln: google translate ( imparts communicability to individuals whose native tongue differs / mitigates the trials of communication across linguistic barriers / hastens the bridging of semantic boundaries / mollifies the complexity of multilingual communication / avails itself to the internationalization of discussion / flexes its muscles to abet intercultural conversation / calms the tides of linguistic divergence ).
+***
+informal english: corn fields are all across illinois, visible once you leave chicago.
+Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
+***
+informal english: """
+with st.form(key='my_form'):
+    prompt = st.text_area(label='Enter sentence', value=g)
+    submit_button = st.form_submit_button(label='Submit')
+    if submit_button:
+      with torch.no_grad():
+        text = tokenizer.encode(prompt)
+        myinput, past_key_values = torch.tensor([text]), None
+        myinput = myinput
+        #myinput= myinput
+        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
+        logits = logits[0,-1]
+        probabilities = torch.nn.functional.softmax(logits)
+        best_logits, best_indices = logits.topk(number_of_outputs)
+        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
+        text.append(best_indices[0].item())
+        best_probabilities = probabilities[best_indices].tolist()
+        words = []              
+        st.write(best_words)
+        
+        
+