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from smolagents.tools import Tool
from typing import Optional
import os
from transformers import pipeline
import requests
import io
from PIL import Image
#from dotenv import load_dotenv
#load_dotenv()
class TranscriptSummarizer(Tool):
description = "Summarizes a transcript and generates blog content using the transformers library and Hugging Face API for image generation."
name = "transcript_summarizer"
inputs = {'transcript': {'type': 'string', 'description': 'The transcript to summarize.'}}
output_type = "string"
def __init__(self, *args, hf_api_key: str = None, **kwargs):
super().__init__(*args, **kwargs)
self.summarizer = pipeline("summarization", model="google/pegasus-xsum")
self.api_url = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
self.hf_api_key = hf_api_key
self.headers = {"Authorization": f"Bearer {self.hf_api_key}"}
def query(self, payload):
response = requests.post(self.api_url, headers=self.headers, json=payload)
return response.content
def forward(self, transcript: str) -> str:
try:
if not self.hf_api_key:
return "Hugging Face API key is required. Please provide it in the input field."
transcript_length = len(transcript)
def get_summary_lengths(length):
# set the short maths formula
max_length = int(length * 0.8)
min_length = int(length * 0.2)
return max_length, min_length
# Split the transcript into chunks of 500 characters make it dynamic according to the length of the transcript
if transcript_length < 500:
return "Transcript is too short to summarize."
chunk_size = 500
transcript_chunks = [transcript[i:i+chunk_size] for i in range(0, len(transcript), chunk_size)]
# Summarize each chunk of the transcript
summaries = []
for chunk in transcript_chunks:
max_length, min_length = get_summary_lengths(len(chunk))
summary = self.summarizer(chunk, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
summaries.append(summary)
# Concatenate the summaries
full_summary = "\n".join(summaries)
key_entities = full_summary.split()[:15] # Extract first 3 words as key entities
image_prompt = f"Generate an image related to: {' '.join(key_entities)}, cartoon style"
image_bytes = self.query({"inputs": image_prompt})
image = Image.open(io.BytesIO(image_bytes))
image_folder = "Image"
if not os.path.exists(image_folder):
os.makedirs(image_folder)
image_url = os.path.join(image_folder, "image.jpg") # Specify the folder path
image.save(image_url) # Save the image to a file
return f"{full_summary}\n\nImage URL: {image_url}" # Return the file path
except Exception as e:
return f"An unexpected error occurred: {str(e)}"
class YouTubeTranscriptExtractor(Tool):
description = "Extracts the transcript from a YouTube video."
name = "youtube_transcript_extractor"
inputs = {'video_url': {'type': 'string', 'description': 'The URL of the YouTube video.'}}
output_type = "string"
def forward(self, video_url: str) -> str:
try:
from pytubefix import YouTube
# Create a YouTube object
yt = YouTube(video_url)
lang='en'
# Get the video transcript
if lang in yt.captions:
transcript = yt.captions['en'].generate_srt_captions()
else:
transcript = yt.captions.all()[0].generate_srt_captions()
lang = yt.captions.all()[0].code
# Clean up the transcript by removing timestamps and line numbers
cleaned_transcript = ""
for line in transcript.splitlines():
if not line.strip().isdigit() and "-->" not in line:
cleaned_transcript += line + "\n"
print("transcript : ", cleaned_transcript)
return cleaned_transcript
except Exception as e:
return f"An unexpected error occurred: {str(e)}"
def __init__(self, *args, **kwargs):
self.is_initialized = False
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