GraphicAINovel

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awacke1 commited on Mar 4

Commit

53e1c4e

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1 Parent(s): 1f66ab5

Update app.py

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app.py +2 -57

app.py CHANGED Viewed

@@ -1,26 +1,20 @@
 import streamlit as st
 import os
 import json
 import random
-# Imports
 import base64
 import glob
-import json
 import math
 import openai
-import os
 import pytz
 import re
 import requests
-import streamlit as st
 import textract
 import time
 import zipfile
 import huggingface_hub
 import dotenv
-import streamlit.components.v1 as components  # Import Streamlit Components for HTML5
 from audio_recorder_streamlit import audio_recorder
 from bs4 import BeautifulSoup
@@ -29,12 +23,6 @@ from datetime import datetime
 from dotenv import load_dotenv
 from huggingface_hub import InferenceClient
 from io import BytesIO
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationalRetrievalChain
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.memory import ConversationBufferMemory
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
 from openai import ChatCompletion
 from PyPDF2 import PdfReader
 from templates import bot_template, css, user_template
@@ -55,15 +43,6 @@ st.set_page_config(
     }
 )
-experimentalSubProgram="""
-import streamlit as st
-import random
-import numpy as np
-"""
 def SpeechSynthesis(result):
     documentHTML5='''
     <!DOCTYPE html>
@@ -90,9 +69,7 @@ def SpeechSynthesis(result):
     </body>
     </html>
     '''
     components.html(documentHTML5, width=1280, height=300)
-    #return result
 PromptPrefix = 'Create a graphic novel story with streamlit markdown outlines and tables with appropriate emojis for graphic novel rules defining the method steps of play.  Use story structure architect rules using plan, structure and top three dramatic situations matching the theme for topic of '
 PromptPrefix2 = 'Create a streamlit python user app with full code listing to create a UI implementing the usable choose your own adventure graphic novel rules and story using streamlit, session_state, file_uploader, camera_input, on_change = funcction callbacks, randomness and dice rolls using emojis and st.markdown, st.expander, st.columns and other UI controls in streamlit as a game interface and create inline data tables for entities implemented as variables with python list dictionaries for the game rule entities and stats.  Design it as a fun data driven app and show full python code listing for this ruleset and thematic story plot line: '
@@ -944,7 +921,6 @@ def chat_with_model(prompt, document_section='', model_choice='gpt-3.5-turbo'):
     st.write(time.time() - start_time)
     return full_reply_content
-# 12. Embedding VectorDB for LLM query of documents to text to compress inputs and prompt together as Chat memory using Langchain
 @st.cache_resource
 def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
     conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
@@ -1057,24 +1033,15 @@ def get_zip_download_link(zip_file):
     return href
 # 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10
-# My Inference Endpoint
 API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
-# Original
 API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"
 MODEL2 = "openai/whisper-small.en"
 MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
-#headers = {
-#	"Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
-#	"Content-Type": "audio/wav"
-#}
-# HF_KEY = os.getenv('HF_KEY')
 HF_KEY = st.secrets['HF_KEY']
 headers = {
     "Authorization": f"Bearer {HF_KEY}",
     "Content-Type": "audio/wav"
 }
-#@st.cache_resource
 def query(filename):
     with open(filename, "rb") as f:
         data = f.read()
@@ -1119,23 +1086,19 @@ def whisper_main():
             transcript=''
             st.write(transcript)
         # Whisper to GPT: New!! ---------------------------------------------------------------------
         st.write('Reasoning with your inputs with GPT..')
         response = chat_with_model(transcript)
         st.write('Response:')
         st.write(response)
         filename = generate_filename(response, "txt")
         create_file(filename, transcript, response, should_save)
         # Whisper to GPT: New!! ---------------------------------------------------------------------
         # Whisper to Llama:
         response = StreamLLMChatResponse(transcript)
         filename_txt = generate_filename(transcript, "md")
         create_file(filename_txt, transcript, response, should_save)
         filename_wav = filename_txt.replace('.txt', '.wav')
         import shutil
         try:
@@ -1143,30 +1106,12 @@ def whisper_main():
                 shutil.copyfile(filename, filename_wav)
         except:
             st.write('.')
         if os.path.exists(filename):
             os.remove(filename)
-        #st.experimental_rerun()
-        #except:
-        #    st.write('Starting Whisper Model on GPU.  Please retry in 30 seconds.')
-# Sample function to demonstrate a response, replace with your own logic
-def StreamMedChatResponse(topic):
-    st.write(f"Showing resources or questions related to: {topic}")
 # 17. Main
 def main():
-    prompt = f"Write ten funny jokes that are tweet length stories that make you laugh.  Show as markdown outline with emojis for each."
-    # Add Wit and Humor buttons
-    # add_witty_humor_buttons()
-    # add_medical_exam_buttons()
     with st.expander("Prompts 📚", expanded=False):
         example_input = st.text_input("Enter your prompt text for Llama:", value=prompt, help="Enter text to get a response from DromeLlama.")
         if st.button("Run Prompt With Llama model", help="Click to run the prompt."):

 import streamlit as st
+import streamlit.components.v1 as components
 import os
 import json
 import random
 import base64
 import glob
 import math
 import openai
 import pytz
 import re
 import requests
 import textract
 import time
 import zipfile
 import huggingface_hub
 import dotenv
 from audio_recorder_streamlit import audio_recorder
 from bs4 import BeautifulSoup
 from dotenv import load_dotenv
 from huggingface_hub import InferenceClient
 from io import BytesIO
 from openai import ChatCompletion
 from PyPDF2 import PdfReader
 from templates import bot_template, css, user_template
     }
 )
 def SpeechSynthesis(result):
     documentHTML5='''
     <!DOCTYPE html>
     </body>
     </html>
     '''
     components.html(documentHTML5, width=1280, height=300)
 PromptPrefix = 'Create a graphic novel story with streamlit markdown outlines and tables with appropriate emojis for graphic novel rules defining the method steps of play.  Use story structure architect rules using plan, structure and top three dramatic situations matching the theme for topic of '
 PromptPrefix2 = 'Create a streamlit python user app with full code listing to create a UI implementing the usable choose your own adventure graphic novel rules and story using streamlit, session_state, file_uploader, camera_input, on_change = funcction callbacks, randomness and dice rolls using emojis and st.markdown, st.expander, st.columns and other UI controls in streamlit as a game interface and create inline data tables for entities implemented as variables with python list dictionaries for the game rule entities and stats.  Design it as a fun data driven app and show full python code listing for this ruleset and thematic story plot line: '
     st.write(time.time() - start_time)
     return full_reply_content
 @st.cache_resource
 def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
     conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
     return href
 # 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10
 API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
 API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"
 MODEL2 = "openai/whisper-small.en"
 MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
 HF_KEY = st.secrets['HF_KEY']
 headers = {
     "Authorization": f"Bearer {HF_KEY}",
     "Content-Type": "audio/wav"
 }
 def query(filename):
     with open(filename, "rb") as f:
         data = f.read()
             transcript=''
             st.write(transcript)
         # Whisper to GPT: New!! ---------------------------------------------------------------------
         st.write('Reasoning with your inputs with GPT..')
         response = chat_with_model(transcript)
         st.write('Response:')
         st.write(response)
         filename = generate_filename(response, "txt")
         create_file(filename, transcript, response, should_save)
         # Whisper to GPT: New!! ---------------------------------------------------------------------
         # Whisper to Llama:
         response = StreamLLMChatResponse(transcript)
         filename_txt = generate_filename(transcript, "md")
         create_file(filename_txt, transcript, response, should_save)
         filename_wav = filename_txt.replace('.txt', '.wav')
         import shutil
         try:
                 shutil.copyfile(filename, filename_wav)
         except:
             st.write('.')
         if os.path.exists(filename):
             os.remove(filename)
 # 17. Main
 def main():
+    prompt = PromptPrefix2
     with st.expander("Prompts 📚", expanded=False):
         example_input = st.text_input("Enter your prompt text for Llama:", value=prompt, help="Enter text to get a response from DromeLlama.")
         if st.button("Run Prompt With Llama model", help="Click to run the prompt."):