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import streamlit as st
import requests
import pandas as pd
import transformers
from transformers import pipeline
import tensorflow
# Function to search CrossRef using the user's query
def search_crossref(query, rows=10):
url = "https://api.crossref.org/works"
params = {
"query": query,
"rows": rows,
"filter": "type:journal-article"
}
try:
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as e:
st.error(f"HTTP error occurred: {e}")
return None
except Exception as e:
st.error(f"An error occurred: {e}")
return None
# Function to display the results in a table format
def display_results(data):
if data:
items = data.get('message', {}).get('items', [])
if not items:
st.warning("No results found for the query.")
return
paper_list = []
for item in items:
paper = {
"Title": item.get('title', [''])[0],
"Author(s)": ', '.join([author['family'] for author in item.get('author', [])]),
"Journal": item.get('container-title', [''])[0],
"DOI": item.get('DOI', ''),
"Link": item.get('URL', ''),
"Published": item.get('issued', {}).get('date-parts', [[None]])[0][0] if 'issued' in item else "N/A"
}
paper_list.append(paper)
df = pd.DataFrame(paper_list)
st.write(df)
else:
st.warning("No data to display.")
# Function to summarize text using the specified model
def summarize_text(text):
try:
# Initialize the summarization model with PyTorch
summarizer = pipeline("text2text-generation", model="spacemanidol/flan-t5-large-website-summarizer", framework="pt")
summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
return summary[0]['generated_text']
except Exception as e:
st.error(f"An error occurred during summarization: {e}")
return "Summary could not be generated."
# Function to generate text (if you want to keep this)
def generate_text(text):
try:
# Initialize the text generation model with PyTorch
text_generator = pipeline("text2text-generation", model="JorgeSarry/est5-summarize", framework="pt")
generated_text = text_generator(text, max_length=150, min_length=50, do_sample=False)
return generated_text[0]['generated_text']
except Exception as e:
st.error(f"An error occurred during text generation: {e}")
return "Generated text could not be created."
# Main function
if __name__ == "__main__":
# Start Streamlit App
st.title("Research Paper Finder and Text Summarizer")
# Section for Research Paper Finder
st.subheader("Find Research Papers")
query = st.text_input("Enter your research topic or keywords", value="machine learning optimization")
num_papers = st.slider("Select number of papers to retrieve", min_value=5, max_value=50, value=10)
if st.button("Search"):
if query:
with st.spinner('Searching for papers...'):
response_data = search_crossref(query, rows=num_papers)
display_results(response_data)
else:
st.warning("Please enter a search query.")
# Section for Text Summarizer
st.subheader("Summarize Text")
user_text = st.text_area("Enter text to summarize", height=200)
if st.button("Summarize"):
if user_text:
with st.spinner('Summarizing text...'):
summary = summarize_text(user_text)
st.success("Summary:")
st.write(summary)
else:
st.warning("Please enter text to summarize.")
if st.button("Generate Text"):
if user_text:
with st.spinner('Generating text...'):
generated = generate_text(user_text)
st.success("Generated Text:")
st.write(generated)
else:
st.warning("Please enter text to generate from.") |