app.py

import streamlit as st
from transformers import pipeline
import networkx as nx
from pyvis.network import Network
import tempfile
import openai

# ---------------------------
# Model Loading & Caching
# ---------------------------
@st.cache_resource(show_spinner=False)
def load_summarizer():
    # Load a summarization pipeline from Hugging Face (using facebook/bart-large-cnn)
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    return summarizer

@st.cache_resource(show_spinner=False)
def load_text_generator():
    # For a quick demo, we use a smaller text generation model (e.g., GPT-2)
    generator = pipeline("text-generation", model="gpt2")
    return generator

summarizer = load_summarizer()
generator = load_text_generator()

# ---------------------------
# OpenAI Based Idea Generation (Streaming)
# ---------------------------
def generate_ideas_with_openai(prompt, api_key):
    openai.api_key = api_key
    output_text = ""
    # Create a chat completion request for streaming output
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are an expert AI research assistant who generates innovative research ideas."},
            {"role": "user", "content": prompt}
        ],
        stream=True,
    )
    st_text = st.empty()  # Placeholder for streaming output
    for chunk in response:
        if 'choices' in chunk and len(chunk['choices']) > 0:
            delta = chunk['choices'][0]['delta']
            if 'content' in delta:
                text_piece = delta['content']
                output_text += text_piece
                st_text.text(output_text)
    return output_text

def generate_ideas_with_hf(prompt):
    # Use a Hugging Face text-generation pipeline for demo purposes.
    # (This may be less creative compared to GPT-3.5)
    results = generator(prompt, max_length=150, num_return_sequences=1)
    idea_text = results[0]['generated_text']
    return idea_text

# ---------------------------
# Streamlit App Layout
# ---------------------------
st.title("Graph of AI Ideas Application")

st.sidebar.header("Configuration")
generation_mode = st.sidebar.selectbox("Select Idea Generation Mode", 
                                        ["Hugging Face Open Source", "OpenAI GPT-3.5 (Streaming)"])
openai_api_key = st.sidebar.text_input("OpenAI API Key (for GPT-3.5 Streaming)", type="password")

# --- Section 1: Research Paper Input and Idea Generation ---
st.header("Research Paper Input")
paper_abstract = st.text_area("Enter the research paper abstract:", height=200)

if st.button("Generate Ideas"):
    if paper_abstract.strip():
        st.subheader("Summarized Abstract")
        # Summarize the paper abstract to capture essential points
        summary = summarizer(paper_abstract, max_length=100, min_length=30, do_sample=False)
        summary_text = summary[0]['summary_text']
        st.write(summary_text)
        
        st.subheader("Generated Research Ideas")
        # Build a prompt that combines the abstract and its summary
        prompt = (
            f"Based on the following research paper abstract, generate innovative and promising research ideas for future work.\n\n"
            f"Paper Abstract:\n{paper_abstract}\n\n"
            f"Summary:\n{summary_text}\n\n"
            f"Research Ideas:"
        )
        if generation_mode == "OpenAI GPT-3.5 (Streaming)":
            if not openai_api_key.strip():
                st.error("Please provide your OpenAI API Key in the sidebar.")
            else:
                with st.spinner("Generating ideas using OpenAI GPT-3.5..."):
                    ideas = generate_ideas_with_openai(prompt, openai_api_key)
                    st.write(ideas)
        else:
            with st.spinner("Generating ideas using Hugging Face open source model..."):
                ideas = generate_ideas_with_hf(prompt)
                st.write(ideas)
    else:
        st.error("Please enter a research paper abstract.")

# --- Section 2: Knowledge Graph Visualization ---
st.header("Knowledge Graph Visualization")
st.markdown(
    "Simulate a knowledge graph by entering paper details and their citation relationships. "
    "Enter details in CSV format: **PaperID,Title,CitedPaperIDs** (CitedPaperIDs separated by ';'). "
    "Example:\n\n`1,Paper A,2;3`\n`2,Paper B,`\n`3,Paper C,2`"
)
papers_csv = st.text_area("Enter paper details in CSV format:", height=150)

if st.button("Generate Knowledge Graph"):
    if papers_csv.strip():
        import pandas as pd
        from io import StringIO

        # Process the CSV text input
        data = []
        for line in papers_csv.splitlines():
            parts = line.split(',')
            if len(parts) >= 3:
                paper_id = parts[0].strip()
                title = parts[1].strip()
                cited = parts[2].strip()
                cited_list = [c.strip() for c in cited.split(';') if c.strip()]
                data.append({"paper_id": paper_id, "title": title, "cited": cited_list})
        
        if data:
            # Build a directed graph
            G = nx.DiGraph()
            for paper in data:
                G.add_node(paper["paper_id"], title=paper["title"])
                for cited in paper["cited"]:
                    G.add_edge(paper["paper_id"], cited)
            
            st.subheader("Knowledge Graph")
            # Create an interactive visualization using Pyvis
            net = Network(height="500px", width="100%", directed=True)
            for node, node_data in G.nodes(data=True):
                net.add_node(node, label=node_data["title"])
            for source, target in G.edges():
                net.add_edge(source, target)
            # Write and display the network as HTML in Streamlit
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".html")
            net.write_html(temp_file.name)
            with open(temp_file.name, 'r', encoding='utf-8') as f:
                html_content = f.read()
            st.components.v1.html(html_content, height=500)
    else:
        st.error("Please enter paper details for the knowledge graph.")