File size: 26,855 Bytes
0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 af0b644 0d7d701 0df46b2 080f5e3 bf5b316 0d7d701 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 0d7d701 080f5e3 0fa951f 080f5e3 0df46b2 080f5e3 0df46b2 080f5e3 af0b644 0fa951f 080f5e3 0fa951f 0df46b2 080f5e3 0fa951f 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 bf5b316 080f5e3 af0b644 080f5e3 bf5b316 080f5e3 af0b644 080f5e3 0df46b2 080f5e3 bf5b316 080f5e3 af0b644 bf5b316 af0b644 080f5e3 bf5b316 080f5e3 bf5b316 080f5e3 bf5b316 080f5e3 0df46b2 bf5b316 080f5e3 af0b644 080f5e3 0fa951f 080f5e3 0fa951f 080f5e3 0fa951f 0d7d701 080f5e3 0d7d701 0fa951f 0d7d701 080f5e3 0d7d701 080f5e3 0d7d701 080f5e3 0d7d701 0fa951f af0b644 0fa951f 080f5e3 0d7d701 af0b644 080f5e3 0d7d701 af0b644 080f5e3 0fa951f 0d7d701 080f5e3 af0b644 080f5e3 af0b644 080f5e3 af0b644 080f5e3 0d7d701 080f5e3 af0b644 080f5e3 af0b644 080f5e3 0fa951f bf5b316 0df46b2 bf5b316 080f5e3 bf5b316 080f5e3 0fa951f 0df46b2 080f5e3 bf5b316 080f5e3 0df46b2 080f5e3 0fa951f 080f5e3 bf5b316 080f5e3 0df46b2 bf5b316 080f5e3 0fa951f 0df46b2 080f5e3 0fa951f 080f5e3 bf5b316 080f5e3 0df46b2 080f5e3 bf5b316 080f5e3 0df46b2 080f5e3 0fa951f 080f5e3 0fa951f 0df46b2 0fa951f 080f5e3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 |
import base64
import cv2
import glob
import json
import math
import os
import pytz
import random
import re
import requests
import streamlit as st
import streamlit.components.v1 as components
import textract
import time
import zipfile
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import concurrent
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import deque
from datetime import datetime
from dotenv import load_dotenv
from gradio_client import Client
from io import BytesIO
from moviepy import VideoFileClip
from PIL import Image
from PyPDF2 import PdfReader
from templates import bot_template, css, user_template
from urllib.parse import quote
from xml.etree import ElementTree as ET
import openai
from openai import OpenAI
import pandas as pd
# Configuration
Site_Name = 'Scholarly-Article-Document-Search-With-Memory'
title = "🔬🧠ScienceBrain.AI"
helpURL = 'https://huggingface.co/awacke1'
bugURL = 'https://huggingface.co/spaces/awacke1'
icons = Image.open("icons.ico")
st.set_page_config(
page_title=title,
page_icon=icons,
layout="wide",
initial_sidebar_state="auto",
menu_items={'Get Help': helpURL, 'Report a bug': bugURL, 'About': title}
)
# API Configuration
API_KEY = os.getenv('API_KEY')
HF_KEY = os.getenv('HF_KEY')
headers = {"Authorization": f"Bearer {HF_KEY}", "Content-Type": "application/json"}
key = os.getenv('OPENAI_API_KEY')
client = OpenAI(api_key=key, organization=os.getenv('OPENAI_ORG_ID'))
MODEL = "gpt-4o-2024-05-13"
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = MODEL
if "messages" not in st.session_state:
st.session_state.messages = []
if st.button("Clear Session"):
st.session_state.messages = []
# Sidebar Options
should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")
# HTML5 Speech Synthesis
@st.cache_resource
def SpeechSynthesis(result):
documentHTML5 = '''
<!DOCTYPE html>
<html>
<head>
<title>Read It Aloud</title>
<script type="text/javascript">
function readAloud() {
const text = document.getElementById("textArea").value;
const speech = new SpeechSynthesisUtterance(text);
window.speechSynthesis.speak(speech);
}
</script>
</head>
<body>
<h1>🔊 Read It Aloud</h1>
<textarea id="textArea" rows="10" cols="80">
'''
documentHTML5 += result + '''
</textarea>
<br>
<button onclick="readAloud()">🔊 Read Aloud</button>
</body>
</html>
'''
components.html(documentHTML5, width=1280, height=300)
# File Naming and Saving
def generate_filename(prompt, file_type, original_name=None):
central = pytz.timezone('US/Central')
safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
safe_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt).strip()[:50]
if original_name and file_type == "md": # For images
base_name = os.path.splitext(original_name)[0]
file_stem = f"{safe_date_time}_{safe_prompt}_{base_name}"[:100] # Cap at 100 chars
return f"{file_stem}.{file_type}"
file_stem = f"{safe_date_time}_{safe_prompt}"[:100] # Cap at 100 chars
return f"{file_stem}.{file_type}"
def create_and_save_file(content, file_type="md", prompt=None, original_name=None, should_save=True):
if not should_save:
return None
filename = generate_filename(prompt, file_type, original_name)
with open(filename, "w", encoding="utf-8") as f:
f.write(content if not prompt else prompt + "\n\n" + content)
return filename
# Text Processing
def process_text(text_input):
if text_input:
st.session_state.messages.append({"role": "user", "content": text_input})
with st.chat_message("user"):
st.markdown(text_input)
with st.chat_message("assistant"):
completion = client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages],
stream=False
)
response = completion.choices[0].message.content
st.markdown(response)
filename = generate_filename(text_input, "md")
create_and_save_file(response, "md", text_input, should_save=should_save)
st.session_state.messages.append({"role": "assistant", "content": response})
# Image Processing
def process_image(image_input, user_prompt):
original_name = image_input.name
image_bytes = image_input.read()
with open(original_name, "wb") as f:
f.write(image_bytes) # Save original image
base64_image = base64.b64encode(image_bytes).decode("utf-8")
response = client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[
{"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
{"role": "user", "content": [
{"type": "text", "text": user_prompt},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}}
]}
],
temperature=0.0
)
image_response = response.choices[0].message.content
filename = generate_filename(user_prompt, "md", original_name) # Include prompt in filename
create_and_save_file(image_response, "md", user_prompt, original_name, should_save=should_save)
return image_response
# Audio Processing
def process_audio(audio_input, text_input=''):
if audio_input:
audio_bytes = audio_input if isinstance(audio_input, bytes) else audio_input.read()
supported_formats = ['flac', 'm4a', 'mp3', 'mp4', 'mpeg', 'mpga', 'oga', 'ogg', 'wav', 'webm']
file_ext = "wav" if isinstance(audio_input, bytes) else os.path.splitext(audio_input.name)[1][1:].lower()
if file_ext not in supported_formats:
st.error(f"Unsupported format: {file_ext}. Supported formats: {supported_formats}")
return
if len(audio_bytes) > 200 * 1024 * 1024: # 200MB limit
st.error("File exceeds 200MB limit.")
return
with st.spinner("Transcribing audio..."):
try:
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=BytesIO(audio_bytes)
).text
st.session_state.messages.append({"role": "user", "content": transcription})
with st.chat_message("user"):
st.markdown(transcription)
with st.chat_message("assistant"):
completion = client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[{"role": "user", "content": text_input + "\n\nTranscription: " + transcription}]
)
response = completion.choices[0].message.content
st.markdown(response)
filename = generate_filename(transcription, "md")
create_and_save_file(response, "md", text_input, should_save=should_save)
st.session_state.messages.append({"role": "assistant", "content": response})
except openai.BadRequestError as e:
st.error(f"Audio processing error: {str(e)}")
# Video Processing
def save_video(video_input):
with open(video_input.name, "wb") as f:
f.write(video_input.read())
return video_input.name
def process_video(video_path, seconds_per_frame=2):
base64Frames = []
base_video_path, _ = os.path.splitext(video_path)
video = cv2.VideoCapture(video_path)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = video.get(cv2.CAP_PROP_FPS)
frames_to_skip = int(fps * seconds_per_frame)
curr_frame = 0
while curr_frame < total_frames - 1:
video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
success, frame = video.read()
if not success:
break
_, buffer = cv2.imencode(".jpg", frame)
base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
curr_frame += frames_to_skip
video.release()
audio_path = f"{base_video_path}.mp3"
try:
clip = VideoFileClip(video_path)
if clip.audio:
clip.audio.write_audiofile(audio_path, bitrate="32k")
clip.audio.close()
clip.close()
except Exception as e:
st.warning(f"No audio track found or error: {str(e)}")
audio_path = None
return base64Frames, audio_path
def process_audio_and_video(video_input):
if video_input:
video_path = save_video(video_input)
with st.spinner("Extracting frames and audio..."):
base64Frames, audio_path = process_video(video_path)
if audio_path:
with st.spinner("Transcribing video audio..."):
try:
with open(audio_path, "rb") as audio_file:
transcript = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file
).text
with st.chat_message("user"):
st.markdown(f"Video Transcription: {transcript}")
with st.chat_message("assistant"):
response = client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[
{"role": "system", "content": "Summarize the video and its transcript in Markdown."},
{"role": "user", "content": [
"Video frames:", *map(lambda x: {"type": "image_url", "image_url": {"url": f"data:image/jpg;base64,{x}"}}, base64Frames),
{"type": "text", "text": f"Transcription: {transcript}"}
]}
]
)
result = response.choices[0].message.content
st.markdown(result)
filename = generate_filename(transcript, "md")
create_and_save_file(result, "md", "Video summary", should_save=should_save)
except openai.BadRequestError as e:
st.error(f"Video audio processing error: {str(e)}")
else:
st.warning("No audio to transcribe.")
# ArXiv Search
def search_arxiv(query):
client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
response = client.predict(
message=query,
llm_results_use=5,
database_choice="Semantic Search",
llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
api_name="/update_with_rag_md"
)
result = response[0] + response[1]
filename = generate_filename(query, "md")
create_and_save_file(result, "md", query, should_save=should_save)
st.session_state.messages.append({"role": "assistant", "content": result})
return result
# RAG PDF Gallery
def upload_pdf_files_to_vector_store(vector_store_id, pdf_files):
stats = {"total_files": len(pdf_files), "successful_uploads": 0, "failed_uploads": 0, "errors": []}
def upload_single_pdf(file_path):
file_name = os.path.basename(file_path)
try:
with open(file_path, "rb") as f:
file_response = client.files.create(file=f, purpose="assistants")
client.vector_stores.files.create(vector_store_id=vector_store_id, file_id=file_response.id)
return {"file": file_name, "status": "success"}
except Exception as e:
return {"file": file_name, "status": "failed", "error": str(e)}
with ThreadPoolExecutor(max_workers=5) as executor:
futures = [executor.submit(upload_single_pdf, f) for f in pdf_files]
for future in tqdm(concurrent.futures.as_completed(futures), total=len(pdf_files)):
result = future.result()
if result["status"] == "success":
stats["successful_uploads"] += 1
else:
stats["failed_uploads"] += 1
stats["errors"].append(result)
return stats
def create_vector_store(store_name):
vector_store = client.vector_stores.create(name=store_name)
return {"id": vector_store.id, "name": vector_store.name, "created_at": vector_store.created_at, "file_count": vector_store.file_counts.completed}
def generate_questions(pdf_path):
text = ""
with open(pdf_path, "rb") as f:
pdf = PdfReader(f)
for page in pdf.pages:
text += page.extract_text() or ""
prompt = f"Generate a 10-question quiz with answers based only on this document. Format as markdown with numbered questions and answers:\n{text[:2000]}\n\n"
response = client.chat.completions.create(
model="gpt-4o-2024-05-13",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
def process_rag_query(query, vector_store_id):
try:
response = client.chat.completions.create(
model="gpt-4o-2024-05-13",
messages=[{"role": "user", "content": query}],
tools=[{
"type": "file_search",
"file_search": {
"vector_store_ids": [vector_store_id]
}
}],
tool_choice="auto"
)
tool_calls = response.choices[0].message.tool_calls if response.choices[0].message.tool_calls else []
return response.choices[0].message.content, tool_calls
except openai.BadRequestError as e:
st.error(f"RAG query error: {str(e)}")
return None, []
def evaluate_rag(vector_store_id, questions_dict):
k = 5
total_queries = len(questions_dict) * 10 # 10 questions per PDF
correct_retrievals_at_k = 0
reciprocal_ranks = []
average_precisions = []
for filename, quiz in questions_dict.items():
questions = re.findall(r"\d+\.\s(.*?)\n\s*Answer:\s(.*?)\n", quiz, re.DOTALL)
for question, _ in questions:
expected_file = filename
response, tool_calls = process_rag_query(question, vector_store_id)
if not tool_calls:
continue
retrieved_files = [call.arguments.get("file_id", "") for call in tool_calls if "file_search" in call.type][:k]
if expected_file in retrieved_files:
rank = retrieved_files.index(expected_file) + 1
correct_retrievals_at_k += 1
reciprocal_ranks.append(1 / rank)
precisions = [1 if f == expected_file else 0 for f in retrieved_files[:rank]]
average_precisions.append(sum(precisions) / len(precisions))
else:
reciprocal_ranks.append(0)
average_precisions.append(0)
recall_at_k = correct_retrievals_at_k / total_queries if total_queries else 0
mrr = sum(reciprocal_ranks) / total_queries if total_queries else 0
map_score = sum(average_precisions) / total_queries if total_queries else 0
return {"recall@k": recall_at_k, "mrr": mrr, "map": map_score, "k": k}
def rag_pdf_gallery():
st.subheader("RAG PDF Gallery")
pdf_files = st.file_uploader("Upload PDFs", type=["pdf"], accept_multiple_files=True)
if pdf_files:
pdf_paths = [save_video(f) for f in pdf_files] # Reuse save_video for simplicity
with st.spinner("Creating vector store..."):
vector_store_details = create_vector_store("PDF_Gallery_Store")
stats = upload_pdf_files_to_vector_store(vector_store_details["id"], pdf_paths)
st.json(stats)
col1, col2, col3 = st.columns(3)
with col1:
if st.button("📝 Quiz"):
st.session_state["rag_prompt"] = "Generate a 10-question quiz with answers based only on this document."
with col2:
if st.button("📑 Summary"):
st.session_state["rag_prompt"] = "Summarize this per page and output as markdown outline with emojis and numbered outline with multiple levels summarizing everything unique per page in method steps or fact steps."
with col3:
if st.button("🔍 Key Facts"):
st.session_state["rag_prompt"] = "Extract 10 key facts from this document in markdown with emojis."
with st.spinner("Generating questions..."):
questions_dict = {os.path.basename(p): generate_questions(p) for p in pdf_paths}
st.markdown("### Generated Quiz")
for filename, quiz in questions_dict.items():
st.markdown(f"#### {filename}")
st.markdown(quiz)
query = st.text_input("Ask a question about the PDFs:", value=st.session_state.get("rag_prompt", ""))
if query and st.button("Submit RAG Query"):
with st.spinner("Processing RAG query..."):
response, tool_calls = process_rag_query(query, vector_store_details["id"])
if response:
st.markdown(response)
st.write("Retrieved chunks:")
for call in tool_calls:
if "file_search" in call.type:
st.json(call.arguments)
st.rerun()
if st.button("Evaluate RAG Performance"):
with st.spinner("Evaluating..."):
metrics = evaluate_rag(vector_store_details["id"], questions_dict)
st.json(metrics)
# File Sidebar
def FileSidebar():
st.sidebar.title("File Operations")
default_types = [".md", ".png", ".pdf"]
file_types = st.sidebar.multiselect("Filter by type", [".md", ".wav", ".png", ".mp4", ".mp3", ".pdf"], default=default_types)
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]
all_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
if st.sidebar.button("🗑 Delete All Filtered"):
for file in all_files:
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:
ext = os.path.splitext(file)[1].lower()
col1, col2, col3, col4, col5 = st.sidebar.columns([1, 6, 1, 1, 1])
colFollowUp=""
with col1:
icon = "📜" if ext == ".md" else "📄" if ext == ".pdf" else "🖼️" if ext in [".png", ".jpg", ".jpeg"] else "🎵" if ext in [".wav", ".mp3"] else "🎥" if ext == ".mp4" else "📎"
if st.button(icon, key=f"view_{file}"):
with open(file, "rb") as f:
content = f.read()
if ext == ".md":
colFollowUp=ext
#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 in [".png", ".jpg", ".jpeg"]:
st.image(content, use_column_width=True)
elif ext in [".wav", ".mp3"]:
st.audio(content)
elif ext == ".mp4":
st.video(content)
with col2:
st.markdown(get_table_download_link(file), unsafe_allow_html=True)
with col3:
if st.button("📂", key=f"open_{file}"):
with open(file, "rb") as f:
content = f.read()
if ext == ".md":
st.text_area(f"Editing {file}", value=content.decode("utf-8"), height=300, key=f"edit_{file}")
elif ext == ".pdf":
st.download_button("Download PDF to Edit", content, file, "application/pdf")
st.write("PDF editing not supported in-app; download to edit externally.")
elif ext in [".png", ".jpg", ".jpeg"]:
st.image(content, use_column_width=True, caption=f"Viewing {file}")
elif ext in [".wav", ".mp3"]:
st.audio(content, format=f"audio/{ext[1:]}")
elif ext == ".mp4":
st.video(content, format="video/mp4")
with col4:
if st.button("▶️", key=f"run_{file}"):
if ext == ".md":
process_text(open(file, "r", encoding="utf-8").read())
with col5:
if st.button("🗑", key=f"delete_{file}"):
os.remove(file)
st.rerun()
if colFollowUp == ".md":
colFollowUp=ext
st.markdown(content.decode("utf-8"))
SpeechSynthesis(content.decode("utf-8"))
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].lower()
mime_type = "text/markdown" if ext == ".md" else "application/pdf" if ext == ".pdf" else "image/png" if ext in [".png", ".jpg", ".jpeg"] 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>'
# Main Function
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":
default_text = "Create a summary of PDF py libraries and usage in py with emojis in markdown. Maybe a buckeyball feature rating comparing them against each other in markdown emoji outline or tables."
col1, col2 = st.columns([1, 5])
with col1:
if st.button("📝 MD", key="md_button"):
st.session_state["text_input"] = default_text
with st.spinner("Processing..."):
process_text(default_text)
st.rerun()
with col2:
text_input = st.text_input("Enter your text:", value=st.session_state.get("text_input", ""), key="text_input_field")
if text_input and st.button("Submit Text"):
with st.spinner("Processing..."):
process_text(text_input)
st.rerun()
elif option == "Image":
col1, col2 = st.columns(2)
with col1:
if st.button("📝 Describe"):
st.session_state["image_prompt"] = "Describe this image and list ten facts in a markdown outline with emojis."
with col2:
if st.button("🔍 OCR"):
st.session_state["image_prompt"] = "Show electronic text of text in the image."
text_input = st.text_input("Image Prompt:", value=st.session_state.get("image_prompt", "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 and st.button("Submit Image"):
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)
st.rerun()
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 and text_input and st.button("Submit Audio Recording"):
with open("recorded_audio.wav", "wb") as f:
f.write(audio_bytes)
with st.spinner("Processing..."):
process_audio(audio_bytes, text_input)
st.rerun()
elif audio_input and text_input and st.button("Submit Audio File"):
with st.spinner("Processing..."):
process_audio(audio_input, text_input)
st.rerun()
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 and st.button("Submit Video"):
if video_input.size > 200 * 1024 * 1024:
st.error("Video exceeds 200MB limit.")
else:
with st.spinner("Processing..."):
process_audio_and_video(video_input)
st.rerun()
elif option == "ArXiv Search":
query = st.text_input("AI Search ArXiv Scholarly Articles:")
if query and st.button("Search ArXiv"):
with st.spinner("Searching ArXiv..."):
result = search_arxiv(query)
st.markdown(result)
st.rerun()
elif option == "RAG PDF Gallery":
rag_pdf_gallery()
# Chat Display and Input
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?"):
with st.spinner("Processing..."):
process_text(prompt)
st.rerun()
FileSidebar()
main() |