Create app.py

app.py

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+import gradio as gr
+import os
+import pickle
+import tempfile
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+
+# Load/OpenAI key
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+def build_vectorstore(pdf_path):
+    """Load PDF, chunk it, embed, and create FAISS index"""
+    loader = PyPDFLoader(pdf_path)
+    documents = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        separators=["\n\n", "\n", " ", ""]
+    )
+    chunks = text_splitter.split_documents(documents)
+
+    embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+    vectorstore = FAISS.from_documents(chunks, embeddings)
+    return vectorstore
+
+def rag_bot(question, pdf_file):
+    """Answer user queries using the uploaded PDF"""
+    if pdf_file is None:
+        return "Please upload a PDF first."
+
+    # Save uploaded PDF to a temp file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_pdf:
+        tmp_pdf.write(pdf_file.read())
+        pdf_path = tmp_pdf.name
+
+    try:
+        vectorstore = build_vectorstore(pdf_path)
+        retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+
+        qa = RetrievalQA.from_chain_type(
+            llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0, openai_api_key=OPENAI_API_KEY),
+            chain_type="stuff",
+            retriever=retriever,
+        )
+        result = qa.run(question)
+        return result
+    except Exception as e:
+        return f"Error: {e}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 📖 RAG Q&A Bot – Upload a PDF and Ask Questions")
+    with gr.Row():
+        pdf_file = gr.File(label="Upload PDF", type="file")
+    with gr.Row():
+        question = gr.Textbox(label="Ask a Question")
+    answer = gr.Textbox(label="Answer", interactive=False)
+
+    submit = gr.Button("Submit")
+    submit.click(fn=rag_bot, inputs=[question, pdf_file], outputs=answer)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)