Create app.py

app.py

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+import gradio as gr
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+
+# Initialize the FAISS vector store
+vector_store = None
+
+# Function to handle PDF upload and indexing
+def index_pdf(pdf):
+    global vector_store
+    
+    # Load the PDF
+    loader = PyPDFLoader(pdf.name)
+    documents = loader.load()
+    
+    # Split the documents into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+    
+    # Embed the chunks and store them in the vector store
+    embeddings = OpenAIEmbeddings()
+    vector_store = FAISS.from_documents(texts, embeddings)
+    
+    return "PDF indexed successfully!"
+
+# Function to handle chatbot queries
+def chatbot_query(query):
+    if vector_store is None:
+        return "Please upload and index a PDF first."
+    
+    # Create a retrieval-based QA chain
+    retriever = vector_store.as_retriever()
+    qa_chain = RetrievalQA(llm=OpenAI(), retriever=retriever)
+    
+    # Get the response from the QA chain
+    response = qa_chain.run(query)
+    
+    return response
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Tab("Indexing"):
+        pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
+        index_button = gr.Button("Index PDF")
+        index_output = gr.Textbox(label="Indexing Status")
+        
+        index_button.click(index_pdf, inputs=pdf_input, outputs=index_output)
+    
+    with gr.Tab("Chatbot"):
+        query_input = gr.Textbox(label="Enter your question")
+        query_button = gr.Button("Submit")
+        query_output = gr.Textbox(label="Response")
+        
+        query_button.click(chatbot_query, inputs=query_input, outputs=query_output)
+
+# Launch the Gradio app
+demo.launch()