mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
synced 2026-06-25 21:11:11 +08:00
96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
"""
|
|
Module for converting text to speach
|
|
"""
|
|
from scrapegraphai.utils.save_audio_from_bytes import save_audio_from_bytes
|
|
from ..models import OpenAITextToSpeech
|
|
from .base_graph import BaseGraph
|
|
from ..nodes import (
|
|
FetchNode,
|
|
ParseNode,
|
|
RAGNode,
|
|
GenerateAnswerNode,
|
|
TextToSpeechNode,
|
|
)
|
|
from .abstract_graph import AbstractGraph
|
|
|
|
|
|
class SpeechGraph(AbstractGraph):
|
|
"""
|
|
SpeechSummaryGraph is a tool that automates the process of extracting and summarizing
|
|
information from web pages, then converting that summary into spoken word via an MP3 file.
|
|
"""
|
|
|
|
def __init__(self, prompt: str, source: str, config: dict):
|
|
"""
|
|
Initializes the SmartScraperGraph with a prompt, source, and configuration.
|
|
"""
|
|
super().__init__(prompt, config, source)
|
|
|
|
self.input_key = "url" if source.startswith("http") else "local_dir"
|
|
|
|
def _create_graph(self):
|
|
"""
|
|
Creates the graph of nodes representing the workflow for web scraping and summarization.
|
|
"""
|
|
fetch_node = FetchNode(
|
|
input="url | local_dir",
|
|
output=["doc"],
|
|
)
|
|
parse_node = ParseNode(
|
|
input="doc",
|
|
output=["parsed_doc"],
|
|
node_config={"chunk_size": self.model_token}
|
|
)
|
|
rag_node = RAGNode(
|
|
input="user_prompt & (parsed_doc | doc)",
|
|
output=["relevant_chunks"],
|
|
node_config={
|
|
"llm": self.llm_model,
|
|
"embedder_model": self.embedder_model
|
|
}
|
|
)
|
|
generate_answer_node = GenerateAnswerNode(
|
|
input="user_prompt & (relevant_chunks | parsed_doc | doc)",
|
|
output=["answer"],
|
|
node_config={"llm": self.llm_model},
|
|
)
|
|
text_to_speech_node = TextToSpeechNode(
|
|
input="answer",
|
|
output=["audio"],
|
|
node_config={"tts_model": OpenAITextToSpeech(
|
|
self.config["tts_model"])},
|
|
)
|
|
|
|
return BaseGraph(
|
|
nodes={
|
|
fetch_node,
|
|
parse_node,
|
|
rag_node,
|
|
generate_answer_node,
|
|
text_to_speech_node
|
|
},
|
|
edges={
|
|
(fetch_node, parse_node),
|
|
(parse_node, rag_node),
|
|
(rag_node, generate_answer_node),
|
|
(generate_answer_node, text_to_speech_node)
|
|
},
|
|
entry_point=fetch_node
|
|
)
|
|
|
|
def run(self) -> str:
|
|
"""
|
|
Executes the web scraping, summarization, and text-to-speech process.
|
|
"""
|
|
inputs = {"user_prompt": self.prompt, self.input_key: self.source}
|
|
self.final_state, self.execution_info = self.graph.execute(inputs)
|
|
|
|
audio = self.final_state.get("audio", None)
|
|
if not audio:
|
|
raise ValueError("No audio generated from the text.")
|
|
save_audio_from_bytes(audio, self.config.get(
|
|
"output_path", "output.mp3"))
|
|
print(f"Audio saved to {self.config.get('output_path', 'output.mp3')}")
|
|
|
|
return self.final_state
|