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import gradio as gr
from dotenv import load_dotenv
from gradio import ChatMessage
from deepgram import DeepgramClient, SpeakOptions
from setup import Script, Vector_db, Speech_Text
from langchain_openai import ChatOpenAI
load_dotenv()
bot = Script()
vector = Vector_db()
transcriptor = Speech_Text()
pdf_uploaded = False
output_id = None
# Function to generate chatbot response
def generate_response(chat_history: list[ChatMessage], id=None):
user_input = chat_history[-1]["content"]
if len(chat_history) > 1:
chat = bot.history(chat_history[:-2])
else:
chat = ''
if id is not None:
rag_chain, question = bot.gpt_loaders_id(user_input, chat, id)
else:
rag_chain, question = bot.gpt_loaders(user_input, chat)
return rag_chain.invoke(question)
def process(audio, input_text, pdfs, chat_history: list[ChatMessage]):
global pdf_uploaded, input_pdf, output_id
if pdfs is not None and not pdf_uploaded:
pdf_uploaded = True
pdf_path = pdfs.name
output_id = vector.upload_pdfs_user(pdf_path)
print(output_id)
if pdfs is None:
pdf_uploaded = False
output_id = None
print(output_id)
if audio is not None:
transcript = transcriptor.get_transcript(audio)
chat_history.append({"role": "user", "content": transcript})
elif input_text:
print(input_text)
chat_history.append({"role": "user", "content": input_text})
else:
response = 'Provide a query text or an audio to query.'
chat_history.append({"role": "assistant", "content": response})
audio_data = transcriptor.speech_synthesis(response)
return audio_data, chat_history
response = generate_response(chat_history, output_id)
chat_history.append({"role": "assistant", "content": response})
audio_data = transcriptor.speech_synthesis(response)
return audio_data, chat_history
# Create Gradio Blocks interface
with gr.Blocks() as demo:
gr.Markdown("""
# 🎤 Welcome to the ChatBot
This Bot has a Knowledge base on Indian Taxation Data by default. It allows you to chat with an AI assistant using either **text** or **voice**.<br>You can upload your own PDF data as knowledge base in the **upload a PDF** and can talk to your data seamlessly.
""")
with gr.Row():
with gr.Column(scale=1, min_width=300):
input_pdf = gr.File(label="Upload PDF", file_types=[".pdf"], file_count='single')
gr.Markdown("_Use a PDF to enhance the chatbot's knowledge!_", visible=not pdf_uploaded)
with gr.Row():
chatbot = gr.Chatbot(label="Chatbot Conversation", type="messages", bubble_full_width=True, show_copy_button=True, autoscroll=True)
with gr.Row():
input_textbox = gr.Textbox(label="Input Text", placeholder="Type your message here...")
input_audio = gr.Audio(label="Input Audio", sources="microphone", type="numpy")
process_button = gr.Button("Submit Query")
output_audio = gr.Audio(label="Assistant's Response Audio", interactive=False, autoplay=True)
process_button.click(
fn=process,
inputs=[input_audio, input_textbox, input_pdf, chatbot],
outputs=[output_audio, chatbot]
)
if __name__ == "__main__":
demo.launch() |