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Update app.py
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app.py
CHANGED
@@ -1,46 +1,65 @@
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import streamlit as st
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import openai
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import os
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import json
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import requests
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from datetime import datetime
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from collections import deque
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from openai import ChatCompletion
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from audio_recorder_streamlit import audio_recorder
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# Initialize configurations
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configurations = {}
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config_file = "configurations.json"
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if os.path.exists(config_file):
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with open(config_file, "r") as file:
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configurations = json.load(file)
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openai.api_key = os.getenv('OPENAI_KEY')
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st.set_page_config(page_title="GPT Streamlit Document Reasoner",
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model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
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user_prompt = st.text_area(
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"Enter prompts, instructions & questions:",
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configurations.get("user_prompt", ""),
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height=100
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)
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system_prompt = configurations.get("system_prompt", "You are a helpful assistant.")
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def generate_filename(prompt, file_type):
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safe_prompt = "".join(x for x in prompt if x.isalnum())[:45]
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return f"{safe_date_time}_{safe_prompt}.{file_type}"
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def chat_with_model(prompt, document_section):
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conversation.append({'role': 'user', 'content': prompt})
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if document_section:
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conversation.append({'role': 'assistant', 'content': document_section})
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response = openai.ChatCompletion.create(model=
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return response
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audio_bytes = audio_recorder()
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if audio_bytes:
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filename = generate_filename("Recording", "wav")
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return None
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def create_file(filename, prompt, response):
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def divide_document(document, max_length):
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return [document[i:i+max_length] for i in range(0, len(document), max_length)]
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def main():
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document_sections = deque()
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if uploaded_file is not None:
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document_sections.extend(handle_uploaded_file(uploaded_file, max_length))
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document_responses = {}
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for i, section in enumerate(document_sections):
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if st.button(f"Chat about Section {i+1}"):
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response = chat_with_model(user_prompt, section)
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document_responses[i] = response
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filename = generate_filename(f"{user_prompt}_section_{i+1}", "txt")
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create_file(filename, user_prompt, response)
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if st.button('Chat'):
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response = chat_with_model(user_prompt, ''.join(document_sections))
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filename = generate_filename(user_prompt, "txt")
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create_file(filename, user_prompt, response)
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configurations["user_prompt"] = user_prompt
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configurations["system_prompt"] = system_prompt
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with open(config_file, "w") as file:
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json.dump(configurations, file)
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if __name__ == "__main__":
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main()
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import streamlit as st
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import openai
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import os
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import base64
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import glob
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import json
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import mistune
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import pytz
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import math
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import requests
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from datetime import datetime
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from openai import ChatCompletion
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from xml.etree import ElementTree as ET
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from bs4 import BeautifulSoup
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from collections import deque
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from audio_recorder_streamlit import audio_recorder
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openai.api_key = os.getenv('OPENAI_KEY')
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st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
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menu = ["htm", "txt", "md", "py"]
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choice = st.sidebar.selectbox("Output File Type:", menu)
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model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
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def generate_filename(prompt, file_type):
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central = pytz.timezone('US/Central')
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safe_date_time = datetime.now(central).strftime("%m%d_%I%M")
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safe_prompt = "".join(x for x in prompt if x.isalnum())[:45]
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return f"{safe_date_time}_{safe_prompt}.{file_type}"
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def chat_with_model(prompt, document_section):
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model = model_choice
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conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
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conversation.append({'role': 'user', 'content': prompt})
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if len(document_section)>0:
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conversation.append({'role': 'assistant', 'content': document_section})
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response = openai.ChatCompletion.create(model=model, messages=conversation)
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return response
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#return response['choices'][0]['message']['content']
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def transcribe_audio(openai_key, file_path, model):
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OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
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headers = {
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"Authorization": f"Bearer {openai_key}",
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}
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with open(file_path, 'rb') as f:
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data = {'file': f}
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response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
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if response.status_code == 200:
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st.write(response.json())
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response2 = chat_with_model(response.json().get('text'), '')
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st.write('Responses:')
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#st.write(response)
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st.write(response2)
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return response.json().get('text')
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else:
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st.write(response.json())
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st.error("Error in API call.")
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return None
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def save_and_play_audio(audio_recorder):
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audio_bytes = audio_recorder()
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if audio_bytes:
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filename = generate_filename("Recording", "wav")
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return None
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def create_file(filename, prompt, response):
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if filename.endswith(".txt"):
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with open(filename, 'w') as file:
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file.write(f"Prompt:\n{prompt}\nResponse:\n{response}")
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elif filename.endswith(".htm"):
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with open(filename, 'w') as file:
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file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")
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elif filename.endswith(".md"):
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with open(filename, 'w') as file:
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file.write(f"# Prompt:\n{prompt}\n# Response:\n{response}")
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def truncate_document(document, length):
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return document[:length]
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def divide_document(document, max_length):
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return [document[i:i+max_length] for i in range(0, len(document), max_length)]
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def get_table_download_link(file_path):
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with open(file_path, 'r') as file:
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data = file.read()
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b64 = base64.b64encode(data.encode()).decode()
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file_name = os.path.basename(file_path)
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ext = os.path.splitext(file_name)[1] # get the file extension
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if ext == '.txt':
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mime_type = 'text/plain'
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elif ext == '.htm':
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mime_type = 'text/html'
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elif ext == '.md':
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mime_type = 'text/markdown'
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else:
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mime_type = 'application/octet-stream' # general binary data type
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href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
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return href
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# Audio, transcribe, GPT:
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filename = save_and_play_audio(audio_recorder)
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if filename is not None:
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transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
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st.write(transcription)
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gptOutput = chat_with_model(transcription, '') # push transcript through as prompt
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filename = generate_filename(transcription, choice)
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create_file(filename, transcription, gptOutput)
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st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
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def CompressXML(xml_text):
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root = ET.fromstring(xml_text)
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for elem in list(root.iter()):
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if isinstance(elem.tag, str) and 'Comment' in elem.tag:
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elem.parent.remove(elem)
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return ET.tostring(root, encoding='unicode', method="xml")
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def read_file_content(file,max_length):
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if file.type == "application/json":
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content = json.load(file)
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return str(content)
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elif file.type == "text/html" or file.type == "text/htm":
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content = BeautifulSoup(file, "html.parser")
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return content.text
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elif file.type == "application/xml" or file.type == "text/xml":
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tree = ET.parse(file)
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root = tree.getroot()
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xml = CompressXML(ET.tostring(root, encoding='unicode'))
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return xml
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elif file.type == "text/markdown" or file.type == "text/md":
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md = mistune.create_markdown()
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content = md(file.read().decode())
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return content
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elif file.type == "text/plain":
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return file.getvalue().decode()
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else:
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return ""
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def main():
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user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
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collength, colupload = st.columns([2,3]) # adjust the ratio as needed
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with collength:
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#max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4. 8x=96000 for gpt4-32k.
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max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
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with colupload:
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uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "html", "htm", "md", "txt"])
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document_sections = deque()
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document_responses = {}
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if uploaded_file is not None:
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file_content = read_file_content(uploaded_file, max_length)
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document_sections.extend(divide_document(file_content, max_length))
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if len(document_sections) > 0:
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if st.button("👁️ View Upload"):
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st.markdown("**Sections of the uploaded file:**")
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for i, section in enumerate(list(document_sections)):
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st.markdown(f"**Section {i+1}**\n{section}")
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st.markdown("**Chat with the model:**")
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for i, section in enumerate(list(document_sections)):
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if i in document_responses:
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st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
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else:
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if st.button(f"Chat about Section {i+1}"):
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st.write('Reasoning with your inputs...')
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response = chat_with_model(user_prompt, section)
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st.write('Response:')
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st.write(response)
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document_responses[i] = response
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filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
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create_file(filename, user_prompt, response)
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st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
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if st.button('💬 Chat'):
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st.write('Reasoning with your inputs...')
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response = chat_with_model(user_prompt, ''.join(list(document_sections)))
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st.write('Response:')
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st.write(response)
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filename = generate_filename(user_prompt, choice)
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create_file(filename, user_prompt, response)
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st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
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all_files = glob.glob("*.*")
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all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20] # exclude files with short names
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all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
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for file in all_files:
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col1, col3 = st.sidebar.columns([5,1]) # adjust the ratio as needed
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with col1:
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st.markdown(get_table_download_link(file), unsafe_allow_html=True)
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with col3:
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if st.button("🗑", key="delete_"+file):
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os.remove(file)
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st.experimental_rerun()
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if __name__ == "__main__":
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main()
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