import gradio as gr
from huggingface_hub import InferenceClient
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
import os

# Load the model client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Initialize vector store
vector_store = None

# Preload and process the PDF document
#PDF_PATH = "general symptoms.pdf"  # Path to the pre-defined PDF document

PDF_PATH ="general symptoms.pdf"

def preload_pdf():
    global vector_store

    # Load PDF and extract text
    loader = PyPDFLoader(PDF_PATH)
    documents = loader.load()

    # Split the text into smaller chunks for retrieval
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    docs = text_splitter.split_documents(documents)

    # Compute embeddings for the chunks
    embeddings = HuggingFaceEmbeddings()
    vector_store = FAISS.from_documents(docs, embeddings)

    print(f"PDF '{PDF_PATH}' loaded and indexed successfully.")

# Response generation
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    # max_tokens,
    # temperature,
    # top_p,
):
    global vector_store

    if vector_store is None:
        return "The PDF document is not loaded. Please check the code setup."

    # Retrieve relevant chunks from the PDF
    relevant_docs = vector_store.similarity_search(message, k=3)
    context = "\n".join([doc.page_content for doc in relevant_docs])

    # Combine system message, context, and user message
    full_system_message = (
        f"{system_message}\n\nContext from the document:\n{context}\n\n"
    )

    messages = [{"role": "system", "content": full_system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        # max_tokens=max_tokens,
        stream=True,
        # temperature=temperature,
        # top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

# Gradio interface
#demo = gr.Blocks()

demo = gr.Blocks(css="""

.gr-chat-container {
    display: flex;
    background-color: skyblue;
    justify-content: center;
    align-items: center;
    height: 100vh;
    padding: 20px;
}

.gr-chat {
    height: 80vh;
    width: 70vw;
    justify-content: center;
    align-items: center;
    border: 1px solid #ccc;
    padding: 10px;
    box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1);
    overflow-y: auto;
}
""")


with demo:
    with gr.Row(elem_classes=["gr-chat-container"]):
        with gr.Column(elem_classes=["gr-chat"]):
            chatbot = gr.ChatInterface(
                respond,
                additional_inputs=[
                    gr.Textbox(
                        value=(
                            "You are going to act like a medical practitioner. Hear the symptoms, "
                            "diagnose the disease, mention the disease name as heading, and suggest tips "
                            "to overcome the issue. Base your answers on the provided document. limit the response to 3-4 sentences. list out the response point by point" 
                        ),visible=False,
                        label="System message",
                    ),
                ],
                examples=[
                    ["I am not well and feeling feverish, tired"],
                    ["Can you guide me through quick health tips?"],
                    ["How do I stop worrying about things I can't control?"],
                ],
                title="Diagnify 🕊️",
            )
    
    
if __name__ == "__main__":
    preload_pdf()
    demo.launch()