import gradio as gr import os import logging from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_openai import ChatOpenAI from langchain_community.graphs import Neo4jGraph from typing import List, Tuple from pydantic import BaseModel, Field from langchain_core.messages import AIMessage, HumanMessage from langchain_core.runnables import ( RunnableBranch, RunnableLambda, RunnablePassthrough, RunnableParallel, ) from langchain_core.prompts.prompt import PromptTemplate import requests import tempfile from langchain.memory import ConversationBufferWindowMemory import time import logging from langchain.chains import ConversationChain import torch import torchaudio from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor import numpy as np conversational_memory = ConversationBufferWindowMemory( memory_key='chat_history', k=10, return_messages=True ) # Setup Neo4j graph = Neo4jGraph( url="neo4j+s://6457770f.databases.neo4j.io", username="neo4j", password="Z10duoPkKCtENuOukw3eIlvl0xJWKtrVSr-_hGX1LQ4" ) # Define entity extraction and retrieval functions class Entities(BaseModel): names: List[str] = Field( ..., description="All the person, organization, or business entities that appear in the text" ) entity_prompt = ChatPromptTemplate.from_messages([ ("system", "You are extracting organization and person entities from the text."), ("human", "Use the given format to extract information from the following input: {question}"), ]) chat_model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key=os.environ['OPENAI_API_KEY']) entity_chain = entity_prompt | chat_model.with_structured_output(Entities) def remove_lucene_chars(input: str) -> str: return input.translate(str.maketrans({ "\\": r"\\", "+": r"\+", "-": r"\-", "&": r"\&", "|": r"\|", "!": r"\!", "(": r"\(", ")": r"\)", "{": r"\{", "}": r"\}", "[": r"\[", "]": r"\]", "^": r"\^", "~": r"\~", "*": r"\*", "?": r"\?", ":": r"\:", '"': r'\"', ";": r"\;", " ": r"\ " })) def generate_full_text_query(input: str) -> str: full_text_query = "" words = [el for el in remove_lucene_chars(input).split() if el] for word in words[:-1]: full_text_query += f" {word}~2 AND" full_text_query += f" {words[-1]}~2" return full_text_query.strip() # Setup logging to a file to capture debug information logging.basicConfig(filename='neo4j_retrieval.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') def structured_retriever(question: str) -> str: result = "" entities = entity_chain.invoke({"question": question}) for entity in entities.names: response = graph.query( """CALL db.index.fulltext.queryNodes('entity', $query, {limit:2}) YIELD node,score CALL { WITH node MATCH (node)-[r:!MENTIONS]->(neighbor) RETURN node.id + ' - ' + type(r) + ' -> ' + neighbor.id AS output UNION ALL WITH node MATCH (node)<-[r:!MENTIONS]-(neighbor) RETURN neighbor.id + ' - ' + type(r) + ' -> ' + node.id AS output } RETURN output LIMIT 50 """, {"query": generate_full_text_query(entity)}, ) result += "\n".join([el['output'] for el in response]) return result def retriever_neo4j(question: str): structured_data = structured_retriever(question) logging.debug(f"Structured data: {structured_data}") return structured_data # Setup for condensing the follow-up questions _template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question, in its original language. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) def _format_chat_history(chat_history: list[tuple[str, str]]) -> list: buffer = [] for human, ai in chat_history: buffer.append(HumanMessage(content=human)) buffer.append(AIMessage(content=ai)) return buffer _search_query = RunnableBranch( ( RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config( run_name="HasChatHistoryCheck" ), RunnablePassthrough.assign( chat_history=lambda x: _format_chat_history(x["chat_history"]) ) | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY']) | StrOutputParser(), ), RunnableLambda(lambda x: x["question"]), ) template = """I am a guide for Birmingham, Alabama. I can provide recommendations and insights about the city, including events and activities. Ask your question directly, and I'll provide a precise and quick,short and crisp response in a conversational way without any Greet. {context} Question: {question} Answer:""" qa_prompt = ChatPromptTemplate.from_template(template) # Define the chain for Neo4j-based retrieval and response generation chain_neo4j = ( RunnableParallel( { "context": _search_query | retriever_neo4j, "question": RunnablePassthrough(), } ) | qa_prompt | chat_model | StrOutputParser() ) # Define the function to get the response def get_response(question): try: return chain_neo4j.invoke({"question": question}) except Exception as e: return f"Error: {str(e)}" # Define the function to clear input and output def clear_fields(): return [],"",None # Function to generate audio with Eleven Labs TTS def generate_audio_elevenlabs(text): XI_API_KEY = os.environ['ELEVENLABS_API'] VOICE_ID = 'ehbJzYLQFpwbJmGkqbnW' tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream" headers = { "Accept": "application/json", "xi-api-key": XI_API_KEY } data = { "text": str(text), "model_id": "eleven_multilingual_v2", "voice_settings": { "stability": 1.0, "similarity_boost": 0.0, "style": 0.60, "use_speaker_boost": False } } response = requests.post(tts_url, headers=headers, json=data, stream=True) if response.ok: with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f: for chunk in response.iter_content(chunk_size=1024): if chunk: f.write(chunk) audio_path = f.name logging.debug(f"Audio saved to {audio_path}") return audio_path # Return audio path for automatic playback else: logging.error(f"Error generating audio: {response.text}") return None # Function to add a user's message to the chat history and clear the input box def add_message(history, message): if message.strip(): history.append((message, None)) # Add the user's message to the chat history only if it's not empty return history, "" # Clear the input box # Define function to generate a streaming response def chat_with_bot(messages): user_message = messages[-1][0] # Get the last user message (input) messages[-1] = (user_message, "") # Prepare the placeholder for the bot's response response = get_response(user_message) # Simulate streaming response by iterating over each character in the response for character in response: messages[-1] = (user_message, messages[-1][1] + character) yield messages # Stream each character time.sleep(0.05) # Adjust delay as needed for real-time effect yield messages # Final yield to ensure the full response is displayed # Function to generate audio with Eleven Labs TTS from the last bot response def generate_audio_from_last_response(history): # Get the most recent bot response from the chat history if history and len(history) > 0: recent_response = history[-1][1] # The second item in the tuple is the bot response text if recent_response: return generate_audio_elevenlabs(recent_response) return None # Define example prompts examples = [ ["What are some popular events in Birmingham?"], ["Who are the top players of the Crimson Tide?"], ["Where can I find a hamburger?"], ["What are some popular tourist attractions in Birmingham?"], ["What are some good clubs in Birmingham?"] ] # Function to insert the prompt into the textbox when clicked def insert_prompt(current_text, prompt): return prompt[0] if prompt else current_text # Define the ASR model with Whisper model_id = 'openai/whisper-large-v3' device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype).to(device) processor = AutoProcessor.from_pretrained(model_id) pipe_asr = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, return_timestamps=True ) def transcribe_function(stream, new_chunk): try: sr, y = new_chunk[0], new_chunk[1] except TypeError: print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}") return stream, "", None y = y.astype(np.float32) max_abs_y = np.max(np.abs(y)) if max_abs_y > 0: y = y / max_abs_y if stream is not None: stream = np.concatenate([stream, y]) else: stream = y result = pipe_asr({"array": stream, "sampling_rate": sr}, return_timestamps=False) full_text = result.get("text", "") return stream, full_text, full_text # Create the Gradio Blocks interface with gr.Blocks(theme="rawrsor1/Everforest") as demo: chatbot = gr.Chatbot([], elem_id="RADAR", bubble_full_width=False) with gr.Row(): with gr.Column(): question_input = gr.Textbox(label="Ask a Question", placeholder="Type your question here...") audio_input = gr.Audio(sources=["microphone"],streaming=True,type='numpy',every=0.1,label="Speak to Ask") with gr.Column(): audio_output = gr.Audio(label="Audio", type="filepath", interactive=False) with gr.Row(): with gr.Column(): get_response_btn = gr.Button("Get Response") with gr.Column(): generate_audio_btn = gr.Button("Generate Audio") with gr.Column(): clean_btn = gr.Button("Clean") with gr.Row(): with gr.Column(): gr.Markdown("

Example Prompts

", elem_id="Example-Prompts") gr.Examples(examples=examples, fn=insert_prompt, inputs=question_input, outputs=question_input) # Define interactions # Define interactions for clicking the button get_response_btn.click(fn=add_message, inputs=[chatbot, question_input], outputs=[chatbot, question_input])\ .then(fn=chat_with_bot, inputs=[chatbot], outputs=chatbot) # Define interaction for hitting the Enter key question_input.submit(fn=add_message, inputs=[chatbot, question_input], outputs=[chatbot, question_input])\ .then(fn=chat_with_bot, inputs=[chatbot], outputs=chatbot) # Speech-to-Text functionality state = gr.State() audio_input.stream(transcribe_function, inputs=[state, audio_input], outputs=[state, question_input]) generate_audio_btn.click(fn=generate_audio_from_last_response, inputs=chatbot, outputs=audio_output) clean_btn.click(fn=clear_fields, inputs=[], outputs=[chatbot, question_input, audio_output]) # Launch the Gradio interface demo.launch(show_error=True)