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| import os | |
| import fitz # PyMuPDF | |
| from PIL import Image | |
| from gtts import gTTS | |
| import pygame # Import pygame | |
| import gradio as gr | |
| from dotenv import load_dotenv | |
| from langchain_openai import OpenAIEmbeddings, ChatOpenAI | |
| from langchain_community.vectorstores import Chroma | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain_community.document_loaders import PyPDFLoader | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| # Global variables | |
| count = 0 | |
| n = 0 | |
| chat_history = [] | |
| chain = '' | |
| # Function to set the OpenAI API key | |
| def set_api_key(api_key): | |
| os.environ['OPENAI_API_KEY'] = api_key | |
| return 'OpenAI API key is set' | |
| # Function to enable the API key input box | |
| def enable_api_box(): | |
| return | |
| # Function to add text to the chat history | |
| def add_text(history, text): | |
| if not text: | |
| raise gr.Error('Enter text') | |
| history.append((text, '')) | |
| return history | |
| # Function to process the PDF file and create a conversation chain | |
| def process_file(file): | |
| api_key = os.getenv('OPENAI_API_KEY') | |
| if api_key is None: | |
| raise gr.Error('OpenAI API key not found in environment variables or .env file') | |
| loader = PyPDFLoader(file.name) | |
| documents = loader.load() | |
| # Set the OpenAI API key in the environment variable | |
| os.environ['OPENAI_API_KEY'] = api_key | |
| print("API Key set:", api_key) # Debug print | |
| # Assuming OpenAIEmbeddings uses the environment variable | |
| embeddings = OpenAIEmbeddings() | |
| pdf_search = Chroma.from_documents(documents, embeddings) | |
| chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0.3), | |
| retriever=pdf_search.as_retriever(search_kwargs={"k": 1}), | |
| return_source_documents=True) | |
| return chain | |
| # Function to generate a response based on the chat history and query | |
| def generate_response(history, query, btn): | |
| global count, n, chat_history, chain | |
| if not btn: | |
| raise gr.Error(message='Upload a PDF') | |
| if count == 0: | |
| chain = process_file(btn) | |
| count += 1 | |
| result = chain({"question": query, 'chat_history': chat_history}, return_only_outputs=True) | |
| chat_history.append((query, result["answer"])) | |
| n = list(result['source_documents'][0])[1][1]['page'] | |
| for char in result['answer']: | |
| history[-1][-1] += char | |
| # Generate speech from the answer | |
| generate_speech(result["answer"]) | |
| return history, " " | |
| # Function to render a specific page of a PDF file as an image | |
| def render_file(file): | |
| global n | |
| doc = fitz.open(file.name) | |
| page = doc[n] | |
| pix = page.get_pixmap(matrix=fitz.Matrix(300 / 72, 300 / 72)) | |
| image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples) | |
| return image | |
| # Function to generate speech from text | |
| def generate_speech(text): | |
| tts = gTTS(text=text, lang='en') | |
| tts.save("output.mp3") | |
| play_sound("output.mp3") | |
| def play_sound(file_path): | |
| try: | |
| pygame.mixer.init() | |
| except pygame.error: | |
| print("Unable to initialize audio device. Audio playback will be disabled.") | |
| return | |
| pygame.mixer.music.load(file_path) | |
| pygame.mixer.music.play() | |
| while pygame.mixer.music.get_busy(): | |
| pygame.time.Clock().tick(10) | |
| # Additional cleanup to remove temporary files | |
| def cleanup(): | |
| if os.path.exists("output.mp3"): | |
| os.remove("output.mp3") | |
| def create_demo(): | |
| with gr.Blocks(title="PDF Chatbot", theme="Soft") as demo: | |
| with gr.Column(): | |
| with gr.Row(): | |
| chatbot = gr.Chatbot(value=[], elem_id='chatbot', height=680) | |
| show_img = gr.Image(label='PDF Preview', height=680) | |
| with gr.Row(): | |
| with gr.Column(scale=0.60): | |
| text_input = gr.Textbox( | |
| show_label=False, | |
| placeholder="Ask your pdf?", | |
| container=False | |
| ) | |
| with gr.Column(scale=0.20): | |
| submit_btn = gr.Button('Send') | |
| with gr.Column(scale=0.20): | |
| upload_btn = gr.UploadButton("📁 Upload PDF", file_types=[".pdf"]) | |
| return demo, chatbot, show_img, text_input, submit_btn, upload_btn | |
| if __name__ == '__main__': | |
| # Create the UI components | |
| demo, chatbot, show_img, txt, submit_btn, btn = create_demo() | |
| # Set up the Gradio UI | |
| with demo: | |
| # Upload PDF file and render it as an image | |
| btn.upload(render_file, inputs=[btn], outputs=[show_img]) | |
| # Add text to chat history, generate response, and render file | |
| submit_btn.click(add_text, inputs=[chatbot, txt], outputs=[chatbot], queue=False).\ | |
| success(generate_response, inputs=[chatbot, txt, btn], outputs=[chatbot, txt]).\ | |
| success(render_file, inputs=[btn], outputs=[show_img]) | |
| # Launch the app with text-to-speech cleanup | |
| try: | |
| demo.launch(share=True) | |
| finally: | |
| cleanup() | |