app.py

import gradio as gr
import PyPDF2
from langchain.embeddings import GooglePalmEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import GooglePalm

# Define chatbot response function
def chatbot_response(user_input):
    # Example: returning a placeholder response, update with actual chatbot logic
    # bot_response = "You said: " + user_input
    # history.append((user_input, bot_response))
    return "hii"

# Define text splitter function
def text_splitter_function(text):
    text_splitter = CharacterTextSplitter(
        separator = '\n',
        chunk_size = 1000,
        chunk_overlap = 40,
        length_function = len,
    )
    texts = text_splitter.split_text(text)
    return texts

# Helper function for text processing
def helper(text_splitter):
    db = FAISS.from_texts(text_splitter, embeddings)  # Use 'embeddings' for FAISS
    return 'hi'

# PDF text extraction function
def text_extract(file):
    pdf_reader = PyPDF2.PdfReader(file.name)
    num_pages = len(pdf_reader.pages)
    text = ""
    for page_num in range(num_pages):
        page = pdf_reader.pages[page_num]
        text += page.extract_text() or ""
    text_splitter = text_splitter_function(text)  # Split extracted text into chunks
    result = helper(text_splitter)  # Call helper to process text chunks
    return result

# Define Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Chat with ChatGPT-like Interface")

    chatbot = gr.Chatbot()
    state = gr.State([])

    with gr.Row():
        with gr.Column():
            user_input = gr.Textbox(show_label=False, placeholder="Type your message here...")
            send_btn = gr.Button("Send")
        # with gr.Column():
        #     input_file = gr.File(label="Upload PDF", file_count="single")
        #     submit_btn = gr.Button("Submit")
    
    # Connect submit button to text_extract function
    # submit_btn.click(text_extract, inputs=[input_file], outputs=[user_input])

    # Connect send button to chatbot_response function
    send_btn.click(chatbot_response, inputs=[user_input], outputs=[chatbot])

# Initialize embeddings and launch the app
if __name__ == "__main__":
    google_api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your actual Google API key
    embeddings = GooglePalmEmbeddings(google_api_key=google_api_key)
    demo.launch()