Create app.py

app.py

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+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
+
+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 = []
+
+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
+
+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)]
+
+def generate_embeddings(chunks):
+    return embedding_model.encode(chunks)
+
+def store_embeddings(embeddings, metadata):
+    faiss_index.add(np.array(embeddings))
+    metadata_store.extend(metadata)
+
+def retrieve_relevant_chunks(query, k=5):
+    query_embedding = embedding_model.encode([query])
+    distances, indices = faiss_index.search(query_embedding, k)
+    return [(metadata_store[i], distances[i]) for i in indices[0]]
+
+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 App
+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!")
+
+    if st.button("View Topic Summaries"):
+        for chunk in all_chunks[:3]:
+            st.write(chunk)
+
+    user_question = st.text_input("Ask a question about the uploaded papers:")
+    if user_question:
+        relevant_chunks = retrieve_relevant_chunks(user_question)
+        context = "\n\n".join([chunk['chunk'] for chunk, _ in relevant_chunks])
+        answer = ask_groq_api(user_question, context)
+        st.write("**Answer:**", answer)
+
+    if st.button("Generate Scatter Plot"):
+        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)
+
+    st.text_area("Annotate Your Insights:", height=100, key="annotations")
+