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| import os | |
| import gradio as gr | |
| from langchain_community.document_loaders import TextLoader | |
| from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_text_splitters import RecursiveCharacterTextSplitter | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_huggingface import ChatHuggingFace # Add this to your imports | |
| # 2026 Imports: These now live in the classic/retrieval modules | |
| from langchain_classic.chains import create_retrieval_chain | |
| from langchain_classic.chains.combine_documents import create_stuff_documents_chain | |
| # 1. Setup API Token from Secrets | |
| sec_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| # 2. Load and Process Knowledge Base | |
| # Make sure "knowledge.txt" exists in your Space files! | |
| loader = TextLoader("knowledge.txt") | |
| docs = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) | |
| splits = text_splitter.split_documents(docs) | |
| # 3. Create Vector Store | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| vectorstore = FAISS.from_documents(documents=splits, embedding=embeddings) | |
| retriever = vectorstore.as_retriever() | |
| # 4. Initialize LLM (The Fix: Task="conversational") | |
| llm = HuggingFaceEndpoint( | |
| repo_id="Qwen/Qwen2.5-7B-Instruct", | |
| task="conversational", # Matches the provider's requirement | |
| huggingfacehub_api_token=sec_token, | |
| temperature=0.1, | |
| ) | |
| chat_model = ChatHuggingFace(llm=llm) | |
| # 5. Define the RAG Prompt | |
| system_prompt = ( | |
| "You are a helpful assistant for Examfriendly tuition. " | |
| "Use the following pieces of retrieved context to answer the question. " | |
| "If you don't know the answer, say you don't know. " | |
| "\n\n" | |
| "{context}" | |
| ) | |
| prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", system_prompt), | |
| ("human", "{input}"), | |
| ] | |
| ) | |
| # 6. Build the Chain | |
| question_answer_chain = create_stuff_documents_chain(chat_model, prompt) | |
| rag_chain = create_retrieval_chain(retriever, question_answer_chain) | |
| # 7. Define the Chat Function for Gradio | |
| def chat(message, history): | |
| try: | |
| # ChatHuggingFace expects "input" inside the dictionary | |
| result = rag_chain.invoke({"input": message}) | |
| return result["answer"] | |
| except Exception as e: | |
| return f"System Error: {str(e)}" | |
| # 8. Launch the Interface | |
| demo = gr.ChatInterface( | |
| fn=chat, | |
| title="Examfriendly AI Tutor", | |
| description="Ask me anything about your tuition courses!", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=False) |