Create app.py

app.py

@@ -0,0 +1,85 @@
+import os
+import requests
+import streamlit as st
+from io import BytesIO
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from transformers import pipeline
+import torch
+
+st.set_page_config(page_title="RAG-based PDF Chat", layout="centered", page_icon="📄")
+
+@st.cache_resource
+def load_summarization_pipeline():
+    try:
+        summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=0 if torch.cuda.is_available() else -1)
+        return summarizer
+    except Exception as e:
+        st.error(f"Failed to load the summarization model: {e}")
+        return None
+
+summarizer = load_summarization_pipeline()
+
+PDF_FOLDERS = {
+    "Folder 1": ["https://huggingface.co/username/repo/resolve/main/file1.pdf"]
+}
+
+def fetch_pdf_text_from_folders(pdf_folders):
+    all_text = ""
+    for folder_name, urls in pdf_folders.items():
+        folder_text = f"\n[Folder: {folder_name}]\n"
+        for url in urls:
+            try:
+                response = requests.get(url)
+                response.raise_for_status()
+                pdf_file = BytesIO(response.content)
+                pdf_reader = PdfReader(pdf_file)
+                for page in pdf_reader.pages:
+                    page_text = page.extract_text()
+                    if page_text:
+                        folder_text += page_text
+            except Exception as e:
+                st.error(f"Error fetching PDF from {url}: {e}")
+        all_text += folder_text
+    return all_text
+
+@st.cache_data
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
+    return text_splitter.split_text(text)
+
+@st.cache_resource
+def load_embedding_function():
+    try:
+        return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    except Exception as e:
+        st.error(f"Failed to load embedding model: {e}")
+        return None
+
+embedding_function = load_embedding_function()
+
+@st.cache_resource
+def load_or_create_vector_store(text_chunks):
+    if not text_chunks:
+        st.error("No valid text chunks found.")
+        return None
+    try:
+        return FAISS.from_texts(text_chunks, embedding=embedding_function)
+    except Exception as e:
+        st.error(f"Failed to create or load vector store: {e}")
+        return None
+
+def generate_summary_with_huggingface(query, retrieved_text):
+    summarization_input = f"{query}\n\nRelated information:\n{retrieved_text}"[:1024]
+    try:
+        summary = summarizer(summarization_input, max_length=500, min_length=50, do_sample=False)
+        return summary[0]["summary_text"]
+    except Exception as e:
+        st.error(f"Failed to generate summary: {e}")
+        return "Error generating summary."
+
+def user_input(user_question, vector_store):
+    if vector_store i
+