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import os
import openai
import streamlit as st
import trafilatura
from langchain import PromptTemplate, OpenAI, LLMChain
# Definition of the style
custom_css = """
<style>
.title-text {
color: #0077B6; /* Cambia el color del subtítulo a un azul más claro */
}
</style>
"""
st.sidebar.write("This program uses the OpenAI API to function. Please enter your API key below to use the program. ")
OPENAI_API_KEY = st.sidebar.text_input("API KEY:")
class WebContentSummarizerApp:
def __init__(self):
st.title("WebContent Summarizer")
# Insert the custom CSS into the application
st.markdown(custom_css, unsafe_allow_html=True)
# Use a CSS class for the subtitle
st.markdown('<p class="title-text">With just a simple URL, simplify your online research and content consumption. '
'This AI tool extracts and condenses the essential information from web pages into a concise, easy-to-read paragraph.</p>', unsafe_allow_html=True)
self.url = st.text_input("Paste or type the URL you want to summarize:")
self.run_button = st.button("Summarize")
@staticmethod
def scrape_website(url):
downloaded = trafilatura.fetch_url(url)
main_text = trafilatura.extract(downloaded)
return main_text
def run(self):
if self.run_button:
if self.url:
url = self.url
with st.spinner("Getting information from the page..."):
self.text = self.scrape_website(url)
with st.spinner("Analyzing and organizing..."):
result = self.llm_text_insight()
st.write("Summarized content:")
st.write(result)
else:
st.warning("Please enter a valid URL and your OpenAI API Key.")
def llm_text_insight(self):
openai.api_key = OPENAI_API_KEY
text = self.text
# Split the text into smaller fragments
max_length = 4096
chunks = [text[i:i+max_length] for i in range(0, len(text), max_length)]
results = []
template = """
Please take the following text interprete and transform it into an organized paragraph for a study.
Ensure that the paragraph is clear and coherent. If necessary, add or rearrange ideas to achieve a logical structure.
The text is as follows:
text: {input}
Make sure the text is only one paragraph
"""
llm = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
prompt_template = PromptTemplate.from_template(template=template)
chain = LLMChain(llm=llm, prompt=prompt_template)
for chunk in chunks:
result = chain.predict(input=chunk)
results.append(result)
# Check if results contain more than one paragraph and final result
if len(results) > 1:
alternate_input = " ".join(results)
llm = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
prompt_template = PromptTemplate.from_template(template=template)
chain = LLMChain(llm=llm, prompt=prompt_template)
result = chain.predict(input=alternate_input)
return result
else:
# Concatenation of the results
return " ".join(results)
if __name__ == "__main__":
app = WebContentSummarizerApp()
app.run()
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