Upload csv_rag_gradio_app.py

csv_rag_gradio_app.py

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+import gradio as gr
+import pandas as pd
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.docstore.in_memory import InMemoryDocstore
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+import faiss
+import os
+
+# Set your OpenAI API key here or use a secret in HF Spaces
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "")
+
+# Build the RAG pipeline from a CSV file
+def build_rag(csv_path):
+    loader = CSVLoader(file_path=csv_path)
+    docs = loader.load_and_split()
+    embeddings = OpenAIEmbeddings()
+    index = faiss.IndexFlatL2(len(embeddings.embed_query(" ")))
+    vector_store = FAISS(
+        embedding_function=embeddings,
+        index=index,
+        docstore=InMemoryDocstore(),
+        index_to_docstore_id={}
+    )
+    vector_store.add_documents(documents=docs)
+    retriever = vector_store.as_retriever()
+    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+    system_prompt = (
+        "You are an assistant for question-answering tasks. "
+        "Use the following pieces of retrieved context to answer "
+        "the question. If you don't know the answer, say that you "
+        "don't know. Use three sentences maximum and keep the "
+        "answer concise.\n\n{context}"
+    )
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ])
+    question_answer_chain = create_stuff_documents_chain(llm, prompt)
+    rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+    return rag_chain
+
+rag_chain = None
+
+def upload_csv(file):
+    global rag_chain
+    rag_chain = build_rag(file.name)
+    return "CSV loaded and indexed! You can now ask questions."
+
+def ask_question(question):
+    if rag_chain is None:
+        return "Please upload a CSV file first."
+    try:
+        answer = rag_chain.invoke({"input": question})
+        return answer['answer']
+    except Exception as e:
+        return f"Error: {e}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Simple CSV RAG Q&A Bot\nUpload a CSV and ask questions about its content!")
+    with gr.Row():
+        csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
+        upload_btn = gr.Button("Load CSV")
+    status = gr.Textbox(label="Status", interactive=False)
+    question = gr.Textbox(label="Ask a question about your CSV")
+    answer = gr.Textbox(label="Answer", interactive=False)
+    ask_btn = gr.Button("Ask")
+
+    upload_btn.click(upload_csv, inputs=csv_input, outputs=status)
+    ask_btn.click(ask_question, inputs=question, outputs=answer)
+
+demo.launch()