Create app.py

app.py

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+import streamlit as st
+import openai
+import os
+import base64
+import glob
+import json
+import mistune
+import pytz
+import math
+import requests
+import pandas as pd
+
+from datetime import datetime
+from openai import ChatCompletion
+from xml.etree import ElementTree as ET
+from bs4 import BeautifulSoup
+from collections import deque
+from audio_recorder_streamlit import audio_recorder
+
+openai.api_key = os.getenv('OPENAI_KEY')
+st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
+
+menu = ["txt", "htm", "md", "py", "csv", "xlsx"]
+choice = st.sidebar.selectbox("Output File Type:", menu)
+model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
+
+def generate_filename(prompt, file_type):
+    central = pytz.timezone('US/Central')
+    safe_date_time = datetime.now(central).strftime("%m%d_%I%M")  
+    safe_prompt = "".join(x for x in prompt if x.isalnum())[:45]
+    return f"{safe_date_time}_{safe_prompt}.{file_type}"
+
+TEMPERATURE = st.sidebar.slider("Adjust Creativity:", min_value=0.1, max_value=1.0, value=0.5, step=0.1)
+def chat_with_model(prompt, document_section):
+    model = model_choice
+    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
+    conversation.append({'role': 'user', 'content': prompt})
+    conversation.append({'role': 'assistant', 'content': document_section})
+    response = openai.ChatCompletion.create(model=model, messages=conversation, temperature=TEMPERATURE)
+    return response['choices'][0]['message']['content']
+    
+def create_file(filename, prompt, response):
+    if filename.endswith(".txt"):
+        with open(filename, 'w') as file:
+            file.write(f"Prompt:\n{prompt}\nResponse:\n{response}")
+    elif filename.endswith(".htm"):
+        with open(filename, 'w') as file:
+            file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")
+    elif filename.endswith(".md"):
+        with open(filename, 'w') as file:
+            file.write(f"# Prompt:\n{prompt}\n# Response:\n{response}")
+    elif filename.endswith(".csv"):
+        response_df = pd.DataFrame({"Prompt": [prompt], "Response": [response]})
+        response_df.to_csv(filename, index=False)
+    elif filename.endswith(".xlsx"):
+        response_df = pd.DataFrame({"Prompt": [prompt], "Response": [response]})
+        response_df.to_excel(filename, index=False)
+    
+# Updated to auto process transcript to chatgpt in AI pipeline from Whisper to ChatGPT
+def transcribe_audio(openai_key, file_path, model):
+    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
+    headers = {
+        "Authorization": f"Bearer {openai_key}",
+    }
+    with open(file_path, 'rb') as f:
+        data = {'file': f}
+        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
+    if response.status_code == 200:
+        st.write('Reasoning with your transcription..')
+        transcript=response.json().get('text')
+        st.write(transcript)
+        gptResponse = chat_with_model(transcript, '') # send transcript to ChatGPT
+        filename = generate_filename(transcript, choice) # auto name file with date and prompt per output file type
+        create_file(filename, transcript, gptResponse) # write output file 
+        return gptResponse
+    else:
+        st.write(response.json())
+        st.error("Error in API call.")
+        return None
+
+# Updated to call direct from transcription to chat inference.
+def save_and_play_audio(audio_recorder):
+    audio_bytes = audio_recorder()
+    if audio_bytes:
+        filename = generate_filename("Recording", "wav")
+        with open(filename, 'wb') as f:
+            f.write(audio_bytes)
+        st.audio(audio_bytes, format="audio/wav")
+        return filename
+USEAUDIO=False
+if USEAUDIO:
+    if st.sidebar.checkbox('Use Audio Input'):
+        filename = save_and_play_audio(audio_recorder)
+        if filename is not None:
+            #if st.button("Transcribe"):
+            transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
+            st.markdown('### Transcription:')
+            st.write(transcription)
+        
+
+def truncate_document(document, length):
+    return document[:length]
+
+def divide_document(document, max_length):
+    return [document[i:i+max_length] for i in range(0, len(document), max_length)]
+
+def get_table_download_link(file_path):
+    with open(file_path, 'r') as file:
+        data = file.read()
+    b64 = base64.b64encode(data.encode()).decode()  
+    file_name = os.path.basename(file_path)
+    ext = os.path.splitext(file_name)[1]  # get the file extension
+    if ext == '.txt':
+        mime_type = 'text/plain'
+    elif ext == '.wav':
+        mime_type = 'audio/x-wav'
+    elif ext == '.htm':
+        mime_type = 'text/html'
+    elif ext == '.md':
+        mime_type = 'text/markdown'
+    else:
+        mime_type = 'application/octet-stream'  # general binary data type
+    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
+    return href
+
+def CompressXML(xml_text):
+    root = ET.fromstring(xml_text)
+    for elem in list(root.iter()):
+        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
+            elem.parent.remove(elem)
+    return ET.tostring(root, encoding='unicode', method="xml")
+    
+def read_file_content(file,max_length):
+    if file.type == "application/json":
+        content = json.load(file)
+        return str(content)
+    elif file.type == "text/html" or file.type == "text/htm":
+        content = BeautifulSoup(file, "html.parser")
+        return content.text
+    elif file.type == "application/xml" or file.type == "text/xml":
+        tree = ET.parse(file)
+        root = tree.getroot()
+        xml = CompressXML(ET.tostring(root, encoding='unicode'))
+        return xml
+    elif file.type == "text/markdown" or file.type == "text/md":
+        md = mistune.create_markdown()
+        content = md(file.read().decode())
+        return content
+    elif file.type == "text/plain":
+        return file.getvalue().decode()
+    elif file.type == "text/csv":
+        df = pd.read_csv(file)
+        return df.to_string(index=False)
+    elif file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
+        df = pd.read_excel(file)
+        return df.to_string(index=False)
+    else:
+        return ""
+
+def main():
+    # max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4.  8x=96000 for gpt4-32k.
+    max_length = st.sidebar.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
+    
+    colprompt, colupload = st.columns([5,2])  # adjust the ratio as needed
+    with colprompt:
+        user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=150)
+    with colupload:
+        uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "html", "htm", "txt"])
+    
+    document_sections = deque()
+    document_responses = {}
+
+    if uploaded_file is not None:
+        file_content = read_file_content(uploaded_file, max_length)
+        document_sections.extend(divide_document(file_content, max_length))
+
+    if len(document_sections) > 0:
+        
+        if st.button("👁️ View Upload"):
+            st.markdown("**Sections of the uploaded file:**")
+            for i, section in enumerate(list(document_sections)):
+                st.markdown(f"**Section {i+1}**\n{section}")
+        
+        st.markdown("**Chat with the model:**")
+        for i, section in enumerate(list(document_sections)):
+            if i in document_responses:
+                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
+            else:
+                if st.button(f"Chat about Section {i+1}"):
+                    st.write('Reasoning with your inputs...')
+                    response = chat_with_model(user_prompt, section)
+                    st.write('Response:')
+                    st.write(response)
+                    document_responses[i] = response
+                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
+                    create_file(filename, user_prompt, response)
+                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+
+    if st.button('💬 Chat'):
+        st.write('Reasoning with your inputs...')
+        response = chat_with_model(user_prompt, ''.join(list(document_sections)))
+        st.write('Response:')
+        st.write(response)
+        
+        filename = generate_filename(user_prompt, choice)
+        create_file(filename, user_prompt, response)
+        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+
+    all_files = glob.glob("*.*")
+    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
+    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
+    
+    for file in all_files:
+        col1, col3 = st.sidebar.columns([5,1])  # adjust the ratio as needed
+        with col1:
+            try:
+                st.markdown(get_table_download_link(file), unsafe_allow_html=True)
+            except Exception as e:
+                st.error(f"Error occurred while processing file {file}: {str(e)}")
+        with col3:
+            if st.button("🗑", key="delete_"+file):
+                os.remove(file)
+                st.experimental_rerun()
+
+if __name__ == "__main__":
+    main()