Implementation of Coduct Codes and PDF analysis with Lyzr Chatbot program
In this lesson, we present a compatible method of issuing, processing, and the YouTube video documentation is used LyzrThe Advanced Ai-Anized Framework is designed to facilitate the interaction with the text data. Levering Lyzr is accurate visible Chatbot Chatbot on YouTube-Transcription-API and FPDF, users can change the documents of the PDF order and use an adequate commentary of strong communication. It is appropriate for researchers, teachers, and classmates, Lyzr, accelerated the process of logical understanding, summarizing summaries, and making creative questions from multimedia sources.
!pip install lyzr youtube-transcript-api fpdf2 ipywidgets
!apt-get update -qq && apt-get install -y fonts-dejavu-coreWe establish the required area of the course. The first command includes the key to the keython of the Python, including Lyzr for the AI, YouTube-Transcription-API. The second command confirms the Font of Dijava Sans installed in the system to support the full unicode text interpretation within the production of PDF.
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
os.environ['OPENAI_API_KEY'] = "YOUR_OPENAI_API_KEY_HERE"We prepare access with the OPENAI API key. We import OS Modules and Openai, and get an API key for environmental variations (or directly set up OS.nvrn). This setup is important to include Open Openai's powerful models within the Llyzr frame.
import json
from lyzr import ChatBot
from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound, CouldNotRetrieveTranscript
from fpdf import FPDF
from ipywidgets import Textarea, Button, Output, Layout
from IPython.display import display, Markdown
import reSee the Complete letter here
We import the key libraries needed for the meeting. It includes JSON for managing data, the Lyzr Chatbot of the conversation skills conducted by AI, as well as YouTubetranscriptic output documents from YouTube videos. Also, it brings a PDF generation, Ipywidget for the active UI components, with ippython.display translation of text content in writing. Module Reu is also imported in normal processing of processing activities.
def transcript_to_pdf(video_id: str, output_pdf_path: str) -> bool:
"""
Download YouTube transcript (manual or auto) and write it into a PDF
using the system-installed DejaVuSans.ttf for full Unicode support.
Fixed to handle long words and text formatting issues.
"""
try:
entries = YouTubeTranscriptApi.get_transcript(video_id)
except (TranscriptsDisabled, NoTranscriptFound, CouldNotRetrieveTranscript):
try:
entries = YouTubeTranscriptApi.get_transcript(video_id, languages=['en'])
except Exception:
print(f"[!] No transcript for {video_id}")
return False
except Exception as e:
print(f"[!] Error fetching transcript for {video_id}: {e}")
return False
text = "n".join(e['text'] for e in entries).strip()
if not text:
print(f"[!] Empty transcript for {video_id}")
return False
pdf = FPDF()
pdf.add_page()
font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf"
try:
if os.path.exists(font_path):
pdf.add_font("DejaVu", "", font_path)
pdf.set_font("DejaVu", size=10)
else:
pdf.set_font("Arial", size=10)
except Exception:
pdf.set_font("Arial", size=10)
pdf.set_margins(20, 20, 20)
pdf.set_auto_page_break(auto=True, margin=25)
def process_text_for_pdf(text):
text = re.sub(r's+', ' ', text)
text = text.replace('nn', 'n')
processed_lines = []
for paragraph in text.split('n'):
if not paragraph.strip():
continue
words = paragraph.split()
processed_words = []
for word in words:
if len(word) > 50:
chunks = [word[i:i+50] for i in range(0, len(word), 50)]
processed_words.extend(chunks)
else:
processed_words.append(word)
processed_lines.append(' '.join(processed_words))
return processed_lines
processed_lines = process_text_for_pdf(text)
for line in processed_lines:
if line.strip():
try:
pdf.multi_cell(0, 8, line.encode('utf-8', 'replace').decode('utf-8'), align='L')
pdf.ln(2)
except Exception as e:
print(f"[!] Warning: Skipped problematic line: {str(e)[:100]}...")
continue
try:
pdf.output(output_pdf_path)
print(f"[+] PDF saved: {output_pdf_path}")
return True
except Exception as e:
print(f"[!] Error saving PDF: {e}")
return FalseSee the Complete letter here
This work, Transcription_To_PDF text, default converting YouTube video documents into pure, readable PDF documents. Returns text using YouTubetranscriptCriscriptCriptapi, without a kind of unavailable texts, and text formats such as long as long words violate PDF. An employee and confirms the negative support of Unicode through dejavisens font (if any) and enhance the PDF translation document by separating the tallest words and maintaining consistent marriages. Returns the truth when PDF is successfully produced or falsely in case of errors.
def create_interactive_chat(agent):
input_area = Textarea(
placeholder="Type a question…", layout=Layout(width="80%", height="80px")
)
send_button = Button(description="Send", button_style="success")
output_area = Output(layout=Layout(
border="1px solid gray", width="80%", height="200px", overflow='auto'
))
def on_send(btn):
question = input_area.value.strip()
if not question:
return
with output_area:
print(f">> You: {question}")
try:
print("<< Bot:", agent.chat(question), "n")
except Exception as e:
print(f"[!] Error: {e}n")
send_button.on_click(on_send)
display(input_area, send_button, output_area)
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This work, create_intinentiancing_chat, creates a simple and effective interface discussion within Colob. Using the Ipywidget provides a text input site (text) for users to type the questions, the shipping button (button) to create a conversation, and output) to show the conversation. When the user clicks sending, the question-installed question is forwarded to Lyzr Chatbot agent, which lists the answer. This enables users to engage in dynamic sessions Q & A according to transcript analysis, which enables communication as a live dialog and ai model.
def main():
video_ids = ["dQw4w9WgXcQ", "jNQXAC9IVRw"]
processed = []
for vid in video_ids:
pdf_path = f"{vid}.pdf"
if transcript_to_pdf(vid, pdf_path):
processed.append((vid, pdf_path))
else:
print(f"[!] Skipping {vid} — no transcript available.")
if not processed:
print("[!] No PDFs generated. Please try other video IDs.")
return
first_vid, first_pdf = processed[0]
print(f"[+] Initializing PDF-chat agent for video {first_vid}…")
bot = ChatBot.pdf_chat(
input_files=[first_pdf]
)
questions = [
"Summarize the transcript in 2–3 sentences.",
"What are the top 5 insights and why?",
"List any recommendations or action items mentioned.",
"Write 3 quiz questions to test comprehension.",
"Suggest 5 creative prompts to explore further."
]
responses = {}
for q in questions:
print(f"[?] {q}")
try:
resp = bot.chat(q)
except Exception as e:
resp = f"[!] Agent error: {e}"
responses[q] = resp
print(f"[/] {resp}n" + "-"*60 + "n")
with open('responses.json','w',encoding='utf-8') as f:
json.dump(responses,f,indent=2)
md = "# Transcript Analysis Reportnn"
for q,a in responses.items():
md += f"## Q: {q}n{a}nn"
with open('report.md','w',encoding='utf-8') as f:
f.write(md)
display(Markdown(md))
if len(processed) > 1:
print("[+] Generating comparison…")
_, pdf1 = processed[0]
_, pdf2 = processed[1]
compare_bot = ChatBot.pdf_chat(
input_files=[pdf1, pdf2]
)
comparison = compare_bot.chat(
"Compare the main themes of these two videos and highlight key differences."
)
print("[+] Comparison Result:n", comparison)
print("n=== Interactive Chat (Video 1) ===")
create_interactive_chat(bot)
See the Complete letter here
Our main work () works as a core of teaching core. Prococates a list of YouTube video IDs, converts available documentation into PDF files using Transcription_To_PDF function. When the PDFs are produced, the Lyzr PDF-Chat's agent is starting in the first PDF, allows the model to answer previously defined questions such as summarizing the content, identifying the questions for questions. The answers are stored in Agreement.json File and were scheduled in a Marchdown report (report.MD). If multiple PDFs are created, the work compares them to using the lyzr agent to highlight the important difference between videos. Finally, it introduces a practical interface, enables powerful conversations based on written content, indicating the energy of Lyzr for the prominent PDF and AI communication conducted by AI.
if __name__ == "__main__":
main()
We ensure that the main function () only works when the text is specifically performed, not when submitted as module. It is a very good practice in the Python text to control the flow of flow.
In conclusion, by combining Lyzr in our work travel as shown in the instruction, we can change the YouTube videos into understanding, effective information. The power of the smart PDF power makes it easier to remove the important themes and produce complete abbreviations, and until they are able to attract, evaluating applicable content by the interface interface. To accept Lyzr Empower users to open deep details and increases the product when working with video texts, can be educational research, or analysis of creative content.
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