Spaces:
Sleeping
Sleeping
| # import all necessary libraries | |
| import os | |
| import requests | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| import google.generativeai as genai | |
| from langchain_anthropic import ChatAnthropic | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_community.vectorstores.faiss import FAISS | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| # load api keys | |
| load_dotenv() | |
| # load models | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| chat = ChatAnthropic(temperature=0, anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"), model_name="claude-3-opus-20240229") | |
| # Define the API endpoint | |
| url = "https://api.deepgram.com/v1/speak?model=aura-asteria-en" | |
| # Set your Deepgram API key | |
| # Define the headers | |
| api_key = os.getenv("AURA_API_KEY") | |
| headers = { | |
| "Authorization": f"Token {api_key}", | |
| "Content-Type": "application/json" | |
| } | |
| # Define the payload | |
| def get_embeddings(user_query): | |
| embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001") | |
| new_db = FAISS.load_local("faiss_index", embeddings) | |
| docs = new_db.similarity_search(user_query) | |
| return docs | |
| # Function to generate response based on user query | |
| def get_response(chat, prompt, user_query): | |
| system = ( | |
| "You are world best travel advisor. Advice the user in best possible" | |
| ) | |
| human = prompt | |
| prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)]) | |
| docs = get_embeddings(user_query) | |
| chain = prompt | chat | |
| output = chain.invoke( | |
| { | |
| "context": docs, | |
| "question" : user_query | |
| } | |
| ) | |
| return output.content | |
| # Streamlit app layout | |
| def main(): | |
| st.title("Claudestay") | |
| # api_key = st.text_input("Enter Anthropic API Key....") | |
| # chat = ChatAnthropic(temperature=0, anthropic_api_key=api_key, model_name="claude-3-opus-20240229") | |
| prompt = """ | |
| Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in | |
| provided context just say, "answer is not available in the context", don't provide the wrong answer. | |
| You must provide answer in markdown table format.\n\n | |
| Context:\n {context}?\n | |
| Question: \n{question}\n | |
| Answer: | |
| """ | |
| # Input box for user query | |
| user_query = st.text_input("Enter your travel query:") | |
| if st.button("Submit"): | |
| with st.spinner("Fetching data..."): | |
| text_response = get_response(chat, prompt, user_query) | |
| payload = { | |
| "text": text_response | |
| } | |
| # Make the POST request | |
| st.markdown(f"**Response:** {text_response}") | |
| # Check if the request was successful | |
| response = requests.post(url, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| # Save the response content to a file | |
| with open("your_output_file.mp3", "wb") as f: | |
| f.write(response.content) | |
| st.audio(response.content) | |
| print("File saved successfully.") | |
| else: | |
| print(f"Error: {response.status_code} - {response.text}") | |
| if __name__ == "__main__": | |
| main() | |