Create app.py

app.py

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+import os
+import faiss
+import streamlit as st
+from groq import Groq
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from streamlit_option_menu import option_menu
+
+# Groq API Key
+client = Groq(api_key="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")
+
+# Load embedding model
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2') 
+
+# FAISS Index
+dimension = 384  
+index = faiss.IndexFlatL2(dimension)
+
+# Sidebar navigation
+with st.sidebar:
+    selected = option_menu(
+        "Research Article Helper", 
+        ["Home", "Upload PDF", "Summary", "About"], 
+        icons=["house", "file-earmark-arrow-up", "file-earmark-text", "info-circle"], 
+        menu_icon="cast", 
+        default_index=0,
+    )
+
+# App title
+st.title("📚 Research Article Helper")
+st.write("Interact with research articles by uploading PDFs and asking questions.")
+
+# "Upload PDF" Section
+if selected == "Upload PDF":
+    st.subheader("📂 Upload a PDF")
+    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+    if uploaded_file:
+        pdf_reader = PdfReader(uploaded_file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+
+        # Split text into chunks
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        chunks = text_splitter.split_text(text)
+
+        # Encode chunks and store embeddings in FAISS
+        embeddings = embedding_model.encode(chunks)
+        for i, embedding in enumerate(embeddings):
+            index.add(np.array([embedding]))
+
+        st.success(f"✅ Processed {len(chunks)} chunks and stored embeddings in FAISS.")
+        st.session_state['chunks'] = chunks
+
+# "Summary" Section
+if selected == "Summary":
+    st.subheader("📑 Get a Summary of the Uploaded PDF")
+    if 'chunks' in st.session_state:
+        if st.button("Generate Summary"):
+            full_text = " ".join(st.session_state['chunks'][:5])  # Summarizing first 5 chunks as an example
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": f"Provide a concise summary of the following text:\n\n{full_text}",
+                    }
+                ],
+                model="llama3-8b-8192",
+            )
+            summary = chat_completion.choices[0].message.content
+            st.write("### 📋 Summary:")
+            st.write(summary)
+        else:
+            st.info("Click the button above to generate a summary.")
+    else:
+        st.warning("Please upload a PDF in the 'Upload PDF' section first.")
+
+# "About" Section
+if selected == "About":
+    st.subheader("ℹ️ About")
+    st.write("**Research Article Helper** is a tool to interact with research articles by uploading PDFs. Use it to ask questions, generate summaries, and gain insights from scientific documents.")
+    st.write("Built using Streamlit, FAISS, and Groq AI.")