Create app.py

app.py

@@ -0,0 +1,114 @@
+import streamlit as st
+import requests
+import pandas as pd
+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
+        summarizer = pipeline("text2text-generation", model="spacemanidol/flan-t5-large-website-summarizer")
+        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
+        text_generator = pipeline("text2text-generation", model="JorgeSarry/est5-summarize")
+        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.")