app.py

import os
import streamlit as st
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
import matplotlib.pyplot as plt
import numpy as np
from groq import Groq
import faiss

GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)

# Initialize Embedding Model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# Initialize FAISS Index
embedding_dim = 384  # Dimensionality of 'all-MiniLM-L6-v2'
faiss_index = faiss.IndexFlatL2(embedding_dim)

# Store Metadata
metadata_store = []

# Function to extract text from PDFs
def extract_text_from_pdf(pdf_file):
    pdf_reader = PdfReader(pdf_file)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

# Function to chunk text
def chunk_text(text, chunk_size=500):
    words = text.split()
    return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]

# Function to generate embeddings
def generate_embeddings(chunks):
    return embedding_model.encode(chunks)

# Store embeddings in FAISS index
def store_embeddings(embeddings, metadata):
    faiss_index.add(np.array(embeddings))
    metadata_store.extend(metadata)

# Retrieve relevant chunks based on query
def retrieve_relevant_chunks(query, k=5):
    query_embedding = embedding_model.encode([query])
    distances, indices = faiss_index.search(query_embedding, k)
    valid_results = [
        (metadata_store[i], distances[0][j]) for j, i in enumerate(indices[0]) if i < len(metadata_store)
    ]
    return valid_results

# Call Groq API to get answers and research gap analysis
def ask_groq_api(question, context):
    chat_completion = client.chat.completions.create(
        messages=[{"role": "user", "content": f"{context}\n\n{question}"}],
        model="llama3-8b-8192"
    )
    return chat_completion.choices[0].message.content

# Streamlit UI setup
st.title("RAG-Based Research Paper Analyzer")

uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type="pdf")

if uploaded_files:
    all_chunks = []
    all_metadata = []
    for uploaded_file in uploaded_files:
        text = extract_text_from_pdf(uploaded_file)
        chunks = chunk_text(text)
        embeddings = generate_embeddings(chunks)
        metadata = [{"chunk": chunk, "file_name": uploaded_file.name} for chunk in chunks]
        store_embeddings(embeddings, metadata)
        all_chunks.extend(chunks)
        all_metadata.extend(metadata)

    st.success("Files uploaded and processed successfully!")

    # Button to view topic summaries with an emoji
    if st.button("View Topic Summaries", help="Click to view a brief summary of the uploaded papers", icon="📚"):
        for chunk in all_chunks[:3]:
            st.write(chunk)

    # User input for query without the icon
    user_question = st.text_input("Ask a question about the uploaded papers:", help="Ask about specific research details")

    if user_question:
        relevant_chunks = retrieve_relevant_chunks(user_question)
        if relevant_chunks:
            context = "\n\n".join([chunk['chunk'] for chunk, _ in relevant_chunks])
            answer = ask_groq_api(user_question, context)
            st.write("**Answer:**", answer)

            # Implement Research Gap Identification based on inconsistencies between papers
            st.subheader("Research Gap Analysis:", icon="⚠️")
            # We will analyze the chunks and context to identify research gaps
            research_gap = analyze_research_gaps(all_chunks)
            st.write(f"**Research Gaps Identified:** {research_gap}")
        else:
            st.write("No relevant sections found for your question.")

    # Adding an emoji for research gap feature
    if st.button("Identify Research Gaps", help="Find unanswered questions or areas where research is lacking", icon="⚠️"):
        st.write("**Research Gap Analysis:**")
        # Implementing research gap analysis based on comparing papers
        research_gap_analysis = identify_research_gaps(all_chunks)
        st.write(research_gap_analysis)

    # Button to generate scatter plot with a chart emoji
    if st.button("Generate Scatter Plot", icon="📊"):
        st.write("Generating scatter plot for methods vs. results...")
        # Example scatter plot (replace with real data)
        x = np.random.rand(10)
        y = np.random.rand(10)
        plt.scatter(x, y)
        plt.xlabel("Methods")
        plt.ylabel("Results")
        st.pyplot(plt)

    # Text area for annotations without the icon
    st.text_area("Annotate Your Insights:", height=100, key="annotations", help="Add your thoughts or comments here")

# Function to analyze and identify research gaps by comparing chunks from different papers
def analyze_research_gaps(chunks):
    # Here we would compare text from different papers to identify discrepancies
    gaps = []
    for i, chunk_1 in enumerate(chunks):
        for j, chunk_2 in enumerate(chunks):
            if i != j:
                # Simple heuristic to compare chunks for inconsistencies or gaps
                if chunk_1[:100] != chunk_2[:100]:  # Checking first 100 characters for difference
                    gaps.append(f"Potential inconsistency between chunk {i} and chunk {j}.")
    return "\n".join(gaps) if gaps else "No major inconsistencies found."

# Function to identify unanswered questions based on comparative analysis of multiple papers
def identify_research_gaps(chunks):
    unanswered_questions = []
    # Simulate a simple search for keywords related to unanswered questions
    for chunk in chunks:
        if "future research" in chunk or "unanswered questions" in chunk:
            unanswered_questions.append(chunk)
    return "\n".join(unanswered_questions) if unanswered_questions else "No specific unanswered questions found."