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| import os | |
| from langchain.chains import RetrievalQA | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_openai import ChatOpenAI | |
| from langchain_community.vectorstores import FAISS | |
| import gradio as gr | |
| # Load secrets | |
| openai_api_key = os.environ.get("OPENAI_API_KEY") | |
| app_password = os.environ.get("APP_PASSWORD") | |
| app_username = os.environ.get("APP_USERNAME", "user") | |
| if not openai_api_key: | |
| raise ValueError("OPENAI_API_KEY is not set.") | |
| if not app_password: | |
| raise ValueError("APP_PASSWORD is not set.") | |
| # Load vectorstore | |
| def load_vectorstore(): | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| return FAISS.load_local(".", embeddings, allow_dangerous_deserialization=True) | |
| vectorstore = load_vectorstore() | |
| # Load LLM | |
| llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.1) | |
| # Setup QA chain | |
| qa_chain = RetrievalQA.from_chain_type( | |
| llm=llm, | |
| retriever=vectorstore.as_retriever(), | |
| return_source_documents=True | |
| ) | |
| def chat_with_pdf(query): | |
| if not query.strip(): | |
| return "❗ Please enter a question." | |
| result = qa_chain.invoke({"query": query}) | |
| answer = result["result"] | |
| sources = result.get("source_documents", []) | |
| if sources: | |
| formatted_sources = "".join( | |
| f"\n\n📄 Source {i+1}:\n{doc.page_content.strip()}" | |
| for i, doc in enumerate(sources) | |
| ) | |
| return f"🧠 **Answer:**\n{answer}{formatted_sources}" | |
| else: | |
| return f"🧠 **Answer:**\n{answer}\n\nℹ️ No source documents found." | |
| # App state | |
| authenticated_users = set() | |
| def login(username, password): | |
| if username == app_username and password == app_password: | |
| authenticated_users.add(username) | |
| return gr.update(visible=True), gr.update(visible=False), "", "" | |
| else: | |
| return gr.update(visible=False), gr.update(visible=True), "", "❌ Invalid credentials." | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🔐 Secure Chatbot") | |
| with gr.Group(visible=True) as login_box: | |
| gr.Markdown("### Login to access the chatbot") | |
| login_user = gr.Textbox(label="Username") | |
| login_pass = gr.Textbox(label="Password", type="password") | |
| login_button = gr.Button("Login") | |
| login_message = gr.Markdown("") | |
| with gr.Group(visible=False) as chat_box: | |
| gr.Markdown("## 📚 Ask questions ") | |
| query_input = gr.Textbox(label="Question", lines=2, placeholder="Type your question here...") | |
| submit_button = gr.Button("Submit") | |
| output = gr.Markdown() | |
| login_button.click( | |
| fn=login, | |
| inputs=[login_user, login_pass], | |
| outputs=[chat_box, login_box, login_user, login_message] | |
| ) | |
| submit_button.click(fn=chat_with_pdf, inputs=query_input, outputs=output) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) | |