Create app.py

app.py

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+
+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.chat_models import ChatOpenAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.docstore.document import Document
+
+# Streamlit UI for OpenAI API Key
+st.title("📄 Chat with PDFs")
+st.sidebar.title("Configuration")
+
+# OpenAI API Key input
+openai_api_key = st.sidebar.text_input(
+    "Enter your OpenAI API Key:", type="password"
+)
+
+if not openai_api_key:
+    st.warning("Please enter your OpenAI API Key in the sidebar.")
+else:
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
+
+    # File upload
+    uploaded_files = st.file_uploader(
+        "Upload one or more PDF files",
+        type="pdf",
+        accept_multiple_files=True
+    )
+
+    if uploaded_files:
+        def extract_text_from_pdfs(uploaded_files):
+            """Extract text content from uploaded PDF files."""
+            all_text = ""
+            for uploaded_file in uploaded_files:
+                pdf_reader = PdfReader(uploaded_file)
+                for page in pdf_reader.pages:
+                    all_text += page.extract_text()
+            return all_text
+
+        def split_text_into_documents(text, chunk_size=1000, overlap=200):
+            """Split long text into manageable chunks."""
+            chunks = []
+            for i in range(0, len(text), chunk_size - overlap):
+                chunk = text[i:i + chunk_size]
+                chunks.append(Document(page_content=chunk))
+            return chunks
+
+        st.info("Extracting text from PDFs...")
+        raw_text = extract_text_from_pdfs(uploaded_files)
+        st.success("Text extracted successfully!")
+
+        # Split text into chunks
+        st.info("Splitting text into smaller chunks...")
+        documents = split_text_into_documents(raw_text)
+        st.success(f"Text split into {len(documents)} chunks.")
+
+        # Ask questions
+        st.subheader("Ask questions about your PDFs:")
+        question = st.text_input("Enter your question:")
+
+        if question:
+            # Load QA chain
+            chain = load_qa_chain(llm, chain_type="stuff")
+            st.info("Fetching the answer...")
+
+            # Get the answer
+            answer = chain.run(input_documents=documents, question=question)
+            st.success(f"Answer: {answer}")