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import base64 |
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import cv2 |
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import glob |
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import json |
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import math |
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import os |
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import pytz |
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import random |
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import re |
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import requests |
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import streamlit as st |
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import streamlit.components.v1 as components |
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import textract |
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import time |
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import zipfile |
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from concurrent.futures import ThreadPoolExecutor |
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from tqdm import tqdm |
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import concurrent |
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from audio_recorder_streamlit import audio_recorder |
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from bs4 import BeautifulSoup |
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from collections import deque |
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from datetime import datetime |
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from dotenv import load_dotenv |
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from gradio_client import Client |
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from io import BytesIO |
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from moviepy import VideoFileClip |
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from PIL import Image |
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from PyPDF2 import PdfReader |
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from templates import bot_template, css, user_template |
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from urllib.parse import quote |
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from xml.etree import ElementTree as ET |
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import openai |
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from openai import OpenAI |
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import pandas as pd |
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Site_Name = 'Scholarly-Article-Document-Search-With-Memory' |
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title = "🔬🧠ScienceBrain.AI" |
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helpURL = 'https://huggingface.co/awacke1' |
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bugURL = 'https://huggingface.co/spaces/awacke1' |
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icons = Image.open("icons.ico") |
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st.set_page_config( |
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page_title=title, |
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page_icon=icons, |
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layout="wide", |
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initial_sidebar_state="auto", |
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menu_items={'Get Help': helpURL, 'Report a bug': bugURL, 'About': title} |
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) |
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API_KEY = os.getenv('API_KEY') |
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HF_KEY = os.getenv('HF_KEY') |
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headers = {"Authorization": f"Bearer {HF_KEY}", "Content-Type": "application/json"} |
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key = os.getenv('OPENAI_API_KEY') |
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client = OpenAI(api_key=key, organization=os.getenv('OPENAI_ORG_ID')) |
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MODEL = "gpt-4o-2024-05-13" |
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if "openai_model" not in st.session_state: |
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st.session_state["openai_model"] = MODEL |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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if st.button("Clear Session"): |
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st.session_state.messages = [] |
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should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.") |
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@st.cache_resource |
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def SpeechSynthesis(result): |
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documentHTML5 = ''' |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Read It Aloud</title> |
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<script type="text/javascript"> |
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function readAloud() { |
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const text = document.getElementById("textArea").value; |
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const speech = new SpeechSynthesisUtterance(text); |
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window.speechSynthesis.speak(speech); |
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} |
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</script> |
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</head> |
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<body> |
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<h1>🔊 Read It Aloud</h1> |
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<textarea id="textArea" rows="10" cols="80"> |
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''' |
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documentHTML5 += result + ''' |
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</textarea> |
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<br> |
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<button onclick="readAloud()">🔊 Read Aloud</button> |
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</body> |
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</html> |
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''' |
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components.html(documentHTML5, width=1280, height=300) |
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def generate_filename(prompt, file_type, original_name=None): |
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central = pytz.timezone('US/Central') |
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safe_date_time = datetime.now(central).strftime("%m%d_%H%M") |
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if original_name: |
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base_name = os.path.splitext(original_name)[0] |
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return f"{safe_date_time}_{base_name}.{file_type}" |
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replaced_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt).strip()[:240] |
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return f"{safe_date_time}_{replaced_prompt}.{file_type}" |
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def create_and_save_file(content, file_type="md", prompt=None, original_name=None, should_save=True): |
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if not should_save: |
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return None |
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filename = generate_filename(prompt, file_type, original_name) |
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with open(filename, "w", encoding="utf-8") as f: |
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f.write(content if not prompt else prompt + "\n\n" + content) |
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return filename |
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def process_text(text_input): |
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if text_input: |
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st.session_state.messages.append({"role": "user", "content": text_input}) |
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with st.chat_message("user"): |
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st.markdown(text_input) |
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with st.chat_message("assistant"): |
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completion = client.chat.completions.create( |
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model=st.session_state["openai_model"], |
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messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages], |
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stream=False |
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) |
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response = completion.choices[0].message.content |
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st.markdown(response) |
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filename = generate_filename(text_input, "md") |
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create_and_save_file(response, "md", text_input, should_save=should_save) |
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st.session_state.messages.append({"role": "assistant", "content": response}) |
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def process_image(image_input, user_prompt): |
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original_name = image_input.name |
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image_bytes = image_input.read() |
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with open(original_name, "wb") as f: |
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f.write(image_bytes) |
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base64_image = base64.b64encode(image_bytes).decode("utf-8") |
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response = client.chat.completions.create( |
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model=st.session_state["openai_model"], |
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messages=[ |
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{"role": "system", "content": "You are a helpful assistant that responds in Markdown."}, |
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{"role": "user", "content": [ |
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{"type": "text", "text": user_prompt}, |
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}} |
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]} |
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], |
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temperature=0.0 |
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) |
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image_response = response.choices[0].message.content |
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filename = generate_filename(user_prompt, "md", original_name) |
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create_and_save_file(image_response, "md", user_prompt, original_name, should_save=should_save) |
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return image_response |
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def process_audio(audio_input, text_input=''): |
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if audio_input: |
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audio_bytes = audio_input.read() |
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supported_formats = ['flac', 'm4a', 'mp3', 'mp4', 'mpeg', 'mpga', 'oga', 'ogg', 'wav', 'webm'] |
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file_ext = os.path.splitext(audio_input.name)[1][1:].lower() |
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if file_ext not in supported_formats: |
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st.error(f"Unsupported format: {file_ext}. Supported formats: {supported_formats}") |
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return |
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if len(audio_bytes) > 200 * 1024 * 1024: |
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st.error("File exceeds 200MB limit.") |
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return |
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with st.spinner("Transcribing audio..."): |
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try: |
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transcription = client.audio.transcriptions.create( |
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model="whisper-1", |
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file=BytesIO(audio_bytes) |
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).text |
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st.session_state.messages.append({"role": "user", "content": transcription}) |
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with st.chat_message("user"): |
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st.markdown(transcription) |
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with st.chat_message("assistant"): |
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completion = client.chat.completions.create( |
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model=st.session_state["openai_model"], |
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messages=[{"role": "user", "content": text_input + "\n\nTranscription: " + transcription}] |
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) |
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response = completion.choices[0].message.content |
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st.markdown(response) |
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filename = generate_filename(transcription, "md") |
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create_and_save_file(response, "md", text_input, should_save=should_save) |
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st.session_state.messages.append({"role": "assistant", "content": response}) |
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except openai.BadRequestError as e: |
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st.error(f"Audio processing error: {str(e)}") |
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def save_video(video_input): |
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with open(video_input.name, "wb") as f: |
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f.write(video_input.read()) |
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return video_input.name |
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def process_video(video_path, seconds_per_frame=2): |
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base64Frames = [] |
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base_video_path, _ = os.path.splitext(video_path) |
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video = cv2.VideoCapture(video_path) |
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total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) |
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fps = video.get(cv2.CAP_PROP_FPS) |
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frames_to_skip = int(fps * seconds_per_frame) |
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curr_frame = 0 |
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while curr_frame < total_frames - 1: |
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video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame) |
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success, frame = video.read() |
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if not success: |
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break |
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_, buffer = cv2.imencode(".jpg", frame) |
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base64Frames.append(base64.b64encode(buffer).decode("utf-8")) |
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curr_frame += frames_to_skip |
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video.release() |
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audio_path = f"{base_video_path}.mp3" |
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try: |
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clip = VideoFileClip(video_path) |
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if clip.audio: |
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clip.audio.write_audiofile(audio_path, bitrate="32k") |
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clip.audio.close() |
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clip.close() |
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except: |
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st.warning("No audio track found in video.") |
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audio_path = None |
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return base64Frames, audio_path |
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def process_audio_and_video(video_input): |
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if video_input: |
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video_path = save_video(video_input) |
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with st.spinner("Extracting frames and audio..."): |
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base64Frames, audio_path = process_video(video_path) |
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if audio_path: |
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with st.spinner("Transcribing video audio..."): |
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try: |
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with open(audio_path, "rb") as audio_file: |
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transcript = client.audio.transcriptions.create( |
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model="whisper-1", |
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file=audio_file |
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).text |
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with st.chat_message("user"): |
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st.markdown(f"Video Transcription: {transcript}") |
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with st.chat_message("assistant"): |
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response = client.chat.completions.create( |
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model=st.session_state["openai_model"], |
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messages=[ |
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{"role": "system", "content": "Summarize the video and its transcript in Markdown."}, |
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{"role": "user", "content": [ |
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"Video frames:", *map(lambda x: {"type": "image_url", "image_url": {"url": f"data:image/jpg;base64,{x}"}}, base64Frames), |
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{"type": "text", "text": f"Transcription: {transcript}"} |
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]} |
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] |
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) |
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result = response.choices[0].message.content |
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st.markdown(result) |
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filename = generate_filename(transcript, "md") |
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create_and_save_file(result, "md", "Video summary", should_save=should_save) |
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except openai.BadRequestError as e: |
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st.error(f"Video audio processing error: {str(e)}") |
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else: |
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st.warning("No audio to transcribe.") |
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def search_arxiv(query): |
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") |
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response = client.predict( |
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message=query, |
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llm_results_use=5, |
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database_choice="Semantic Search", |
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llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2", |
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api_name="/update_with_rag_md" |
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) |
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result = response[0] + response[1] |
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filename = generate_filename(query, "md") |
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create_and_save_file(result, "md", query, should_save=should_save) |
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st.session_state.messages.append({"role": "assistant", "content": result}) |
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return result |
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def upload_pdf_files_to_vector_store(vector_store_id, pdf_files): |
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stats = {"total_files": len(pdf_files), "successful_uploads": 0, "failed_uploads": 0, "errors": []} |
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def upload_single_pdf(file_path): |
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file_name = os.path.basename(file_path) |
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try: |
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with open(file_path, "rb") as f: |
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file_response = client.files.create(file=f, purpose="assistants") |
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client.vector_stores.files.create(vector_store_id=vector_store_id, file_id=file_response.id) |
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return {"file": file_name, "status": "success"} |
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except Exception as e: |
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return {"file": file_name, "status": "failed", "error": str(e)} |
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with ThreadPoolExecutor(max_workers=5) as executor: |
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futures = [executor.submit(upload_single_pdf, f) for f in pdf_files] |
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for future in tqdm(concurrent.futures.as_completed(futures), total=len(pdf_files)): |
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result = future.result() |
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if result["status"] == "success": |
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stats["successful_uploads"] += 1 |
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else: |
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stats["failed_uploads"] += 1 |
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stats["errors"].append(result) |
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return stats |
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def create_vector_store(store_name): |
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vector_store = client.vector_stores.create(name=store_name) |
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return {"id": vector_store.id, "name": vector_store.name, "created_at": vector_store.created_at, "file_count": vector_store.file_counts.completed} |
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def generate_questions(pdf_path): |
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text = "" |
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with open(pdf_path, "rb") as f: |
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pdf = PdfReader(f) |
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for page in pdf.pages: |
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text += page.extract_text() or "" |
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prompt = f"Can you generate a question that can only be answered from this document?:\n{text[:2000]}\n\n" |
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response = client.chat.completions.create( |
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model="gpt-4o-2024-05-13", |
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messages=[{"role": "user", "content": prompt}] |
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) |
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return response.choices[0].message.content |
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def process_rag_query(query, vector_store_id): |
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try: |
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response = client.chat.completions.create( |
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model="gpt-4o-2024-05-13", |
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messages=[{"role": "user", "content": query}], |
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tools=[{"type": "file_search", "file_search": {"vector_store_ids": [vector_store_id]}}], |
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tool_choice="auto" |
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) |
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tool_calls = response.choices[0].message.tool_calls if response.choices[0].message.tool_calls else [] |
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return response.choices[0].message.content, tool_calls |
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except openai.PermissionDeniedError as e: |
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st.error(f"RAG error: {str(e)}. Ensure your project has access to the model.") |
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return None, [] |
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def evaluate_rag(vector_store_id, questions_dict): |
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k = 5 |
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total_queries = len(questions_dict) |
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correct_retrievals_at_k = 0 |
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reciprocal_ranks = [] |
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average_precisions = [] |
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for filename, query in questions_dict.items(): |
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expected_file = filename |
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response, tool_calls = process_rag_query(query, vector_store_id) |
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if not tool_calls: |
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continue |
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retrieved_files = [call.function.arguments.get("file_id", "") for call in tool_calls if "file_search" in call.function.name][:k] |
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if expected_file in retrieved_files: |
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rank = retrieved_files.index(expected_file) + 1 |
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correct_retrievals_at_k += 1 |
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reciprocal_ranks.append(1 / rank) |
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precisions = [1 if f == expected_file else 0 for f in retrieved_files[:rank]] |
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average_precisions.append(sum(precisions) / len(precisions)) |
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else: |
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reciprocal_ranks.append(0) |
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average_precisions.append(0) |
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|
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recall_at_k = correct_retrievals_at_k / total_queries if total_queries else 0 |
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mrr = sum(reciprocal_ranks) / total_queries if total_queries else 0 |
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map_score = sum(average_precisions) / total_queries if total_queries else 0 |
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return {"recall@k": recall_at_k, "mrr": mrr, "map": map_score, "k": k} |
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def rag_pdf_gallery(): |
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st.subheader("RAG PDF Gallery") |
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pdf_files = st.file_uploader("Upload PDFs", type=["pdf"], accept_multiple_files=True) |
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if pdf_files: |
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pdf_paths = [save_video(f) for f in pdf_files] |
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with st.spinner("Creating vector store..."): |
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vector_store_details = create_vector_store("PDF_Gallery_Store") |
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stats = upload_pdf_files_to_vector_store(vector_store_details["id"], pdf_paths) |
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st.json(stats) |
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|
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with st.spinner("Generating evaluation questions..."): |
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|
questions_dict = {os.path.basename(p): generate_questions(p) for p in pdf_paths} |
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st.json(questions_dict) |
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|
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query = st.text_input("Ask a question about the PDFs:") |
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if query: |
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with st.spinner("Processing RAG query..."): |
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|
response, tool_calls = process_rag_query(query, vector_store_details["id"]) |
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|
if response: |
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|
st.markdown(response) |
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|
st.write("Retrieved chunks:") |
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|
for call in tool_calls: |
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|
if "file_search" in call.function.name: |
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|
st.json(call.function.arguments) |
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|
|
|
|
if st.button("Evaluate RAG Performance"): |
|
|
with st.spinner("Evaluating..."): |
|
|
metrics = evaluate_rag(vector_store_details["id"], questions_dict) |
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|
st.json(metrics) |
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|
|
|
|
|
|
def FileSidebar(): |
|
|
st.sidebar.title("File Operations") |
|
|
default_types = [".md", ".png", ".pdf"] |
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|
file_types = st.sidebar.multiselect("Filter by type", [".md", ".wav", ".png", ".mp4", ".mp3", ".pdf"], default=default_types) |
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all_files = [f for f in glob.glob("*.*") if os.path.splitext(f)[1] in file_types and len(os.path.splitext(f)[0]) >= 10] |
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|
all_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) |
|
|
|
|
|
if st.sidebar.button("🗑 Delete All Filtered"): |
|
|
for file in all_files: |
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|
os.remove(file) |
|
|
st.rerun() |
|
|
|
|
|
if st.sidebar.button("⬇️ Download All Filtered"): |
|
|
zip_file = create_zip_of_files(all_files) |
|
|
st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True) |
|
|
|
|
|
for file in all_files: |
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|
ext = os.path.splitext(file)[1] |
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|
col1, col2, col3, col4, col5 = st.sidebar.columns([1, 6, 1, 1, 1]) |
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|
with col1: |
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|
icon = "📜" if ext == ".md" else "📄" if ext == ".pdf" else "🖼️" if ext == ".png" else "🎵" if ext in [".wav", ".mp3"] else "🎥" |
|
|
if st.button(icon, key=f"view_{file}"): |
|
|
with open(file, "rb") as f: |
|
|
content = f.read() |
|
|
if ext == ".md": |
|
|
st.markdown(content.decode("utf-8")) |
|
|
SpeechSynthesis(content.decode("utf-8")) |
|
|
elif ext == ".pdf": |
|
|
st.download_button("Download PDF", content, file, "application/pdf") |
|
|
st.write("PDF Viewer not natively supported; download to view.") |
|
|
elif ext == ".png": |
|
|
st.image(content, use_column_width=True) |
|
|
with col2: |
|
|
st.markdown(get_table_download_link(file), unsafe_allow_html=True) |
|
|
with col3: |
|
|
if st.button("📂", key=f"open_{file}"): |
|
|
st.session_state.update({'filename': file, 'filetext': open(file, "r", encoding="utf-8").read()}) |
|
|
with col4: |
|
|
if st.button("▶️", key=f"run_{file}"): |
|
|
process_text(open(file, "r", encoding="utf-8").read()) |
|
|
with col5: |
|
|
if st.button("🗑", key=f"delete_{file}"): |
|
|
os.remove(file) |
|
|
st.rerun() |
|
|
|
|
|
def create_zip_of_files(files): |
|
|
zip_name = "Files.zip" |
|
|
with zipfile.ZipFile(zip_name, 'w') as zipf: |
|
|
for file in files: |
|
|
zipf.write(file) |
|
|
return zip_name |
|
|
|
|
|
def get_zip_download_link(zip_file): |
|
|
with open(zip_file, 'rb') as f: |
|
|
data = f.read() |
|
|
b64 = base64.b64encode(data).decode() |
|
|
return f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>' |
|
|
|
|
|
@st.cache_resource |
|
|
def get_table_download_link(file_path): |
|
|
with open(file_path, 'rb') as f: |
|
|
data = f.read() |
|
|
b64 = base64.b64encode(data).decode() |
|
|
file_name = os.path.basename(file_path) |
|
|
ext = os.path.splitext(file_name)[1] |
|
|
mime_type = "text/markdown" if ext == ".md" else "application/pdf" if ext == ".pdf" else "image/png" if ext == ".png" else "audio/wav" if ext == ".wav" else "audio/mpeg" if ext == ".mp3" else "video/mp4" if ext == ".mp4" else "application/octet-stream" |
|
|
return f'<a href="data:{mime_type};base64,{b64}" download="{file_name}">{file_name}</a>' |
|
|
|
|
|
|
|
|
def main(): |
|
|
st.markdown("##### GPT-4o Omni Model: Text, Audio, Image, Video & RAG") |
|
|
model_options = ["gpt-4o-2024-05-13", "gpt-3.5-turbo"] |
|
|
st.session_state["openai_model"] = st.selectbox("Select GPT Model", model_options, index=0) |
|
|
|
|
|
option = st.selectbox("Select Input Type", ("Text", "Image", "Audio", "Video", "ArXiv Search", "RAG PDF Gallery")) |
|
|
|
|
|
if option == "Text": |
|
|
text_input = st.text_input("Enter your text:") |
|
|
if text_input: |
|
|
with st.spinner("Processing..."): |
|
|
process_text(text_input) |
|
|
|
|
|
elif option == "Image": |
|
|
text_input = st.text_input("Image Prompt:", value="Describe this image and list ten facts in a markdown outline with emojis.") |
|
|
image_input = st.file_uploader("Upload an image (max 200MB)", type=["png", "jpg", "jpeg"], accept_multiple_files=False) |
|
|
if image_input and text_input: |
|
|
if image_input.size > 200 * 1024 * 1024: |
|
|
st.error("Image exceeds 200MB limit.") |
|
|
else: |
|
|
with st.spinner("Processing..."): |
|
|
image_response = process_image(image_input, text_input) |
|
|
with st.chat_message("ai", avatar="🦖"): |
|
|
st.markdown(image_response) |
|
|
|
|
|
elif option == "Audio": |
|
|
text_input = st.text_input("Audio Prompt:", value="Summarize this audio transcription in Markdown.") |
|
|
audio_input = st.file_uploader("Upload an audio file (max 200MB)", type=["mp3", "wav", "flac", "m4a"], accept_multiple_files=False) |
|
|
audio_bytes = audio_recorder() |
|
|
if audio_bytes: |
|
|
with open("recorded_audio.wav", "wb") as f: |
|
|
f.write(audio_bytes) |
|
|
audio_input = open("recorded_audio.wav", "rb") |
|
|
if audio_input and text_input: |
|
|
with st.spinner("Processing..."): |
|
|
process_audio(audio_input, text_input) |
|
|
|
|
|
elif option == "Video": |
|
|
text_input = st.text_input("Video Prompt:", value="Summarize this video and its transcription in Markdown.") |
|
|
video_input = st.file_uploader("Upload a video file (max 200MB)", type=["mp4"], accept_multiple_files=False) |
|
|
if video_input and text_input: |
|
|
if video_input.size > 200 * 1024 * 1024: |
|
|
st.error("Video exceeds 200MB limit.") |
|
|
else: |
|
|
with st.spinner("Processing..."): |
|
|
process_audio_and_video(video_input) |
|
|
|
|
|
elif option == "ArXiv Search": |
|
|
query = st.text_input("AI Search ArXiv Scholarly Articles:") |
|
|
if query: |
|
|
with st.spinner("Searching ArXiv..."): |
|
|
result = search_arxiv(query) |
|
|
st.markdown(result) |
|
|
|
|
|
elif option == "RAG PDF Gallery": |
|
|
rag_pdf_gallery() |
|
|
|
|
|
|
|
|
for message in st.session_state.messages: |
|
|
with st.chat_message(message["role"]): |
|
|
st.markdown(message["content"]) |
|
|
|
|
|
if prompt := st.chat_input("GPT-4o Multimodal ChatBot - What can I help you with?"): |
|
|
process_text(prompt) |
|
|
|
|
|
FileSidebar() |
|
|
main() |
