Create app.py

app.py

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+import gradio as gr
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import LlamaCpp
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain import hub
+
+# Set up callback manager and model parameters
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+n_gpu_layers = 0 
+n_batch = 512 
+
+llm = LlamaCpp(
+    model_path="./models/phi-2.Q2_K.gguf",
+    n_gpu_layers=n_gpu_layers, n_batch=n_batch,
+    n_ctx = 4096,
+    temperature=0.7,
+    max_tokens=4096,
+    top_p=1,
+    callback_manager=callback_manager,
+    verbose=False, 
+)
+
+# Load the prompt
+prompt = hub.pull("rlm/rag-prompt")
+
+# Function to format documents
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+# Main function to process the question and URL
+def get_answer(question, url):
+    # Load data from the provided URL
+    loader = WebBaseLoader(url)
+    data = loader.load()
+
+    # Split the data into small chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
+    all_splits = text_splitter.split_documents(data)
+
+    # Store the data in Vector Store
+    vectorstore = Chroma.from_documents(documents=all_splits, embedding=HuggingFaceEmbeddings())
+    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+    retrieved_docs = retriever.invoke(question)
+
+    rag_chain = (
+        {"context": retriever  | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    answer = ""
+    for chunk in rag_chain.stream(question):
+        answer += chunk
+        yield answer
+    
+    yield answer
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=get_answer,
+    inputs=[gr.Textbox(lines=1, placeholder="Enter your question here..."),
+            gr.Textbox(lines=1, placeholder="Enter the website URL here...")],
+    outputs="text",
+    title="Web-based Question Answering System",
+    description="Ask a question about the content of a webpage and get an answer.",
+    examples=[
+        ["Which are the top 5 companies in the world with their revenue in table format?", "https://www.investopedia.com/biggest-companies-in-the-world-by-market-cap-5212784"]
+    ]
+)
+
+# Launch the app
+iface.launch(share=True)