Delete app.py

app.py

@@ -1,242 +0,0 @@
-import os
-import streamlit as st
-from typing import List, Tuple
-import json
-import uvicorn
-from dotenv import load_dotenv
-load_dotenv()
-from fastapi import FastAPI
-from langchain.agents import AgentExecutor
-from langchain.agents.format_scratchpad import format_to_openai_function_messages
-from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.callbacks import FinalStreamingStdOutCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.pydantic_v1 import BaseModel, Field
-from langchain.schema.messages import AIMessage, HumanMessage
-from langchain.tools.render import format_tool_to_openai_function
-from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
-from langchain_core.runnables import ConfigurableField
-from langchain_core.tools import Tool
-from langserve import add_routes
-from langchain.prompts import PromptTemplate
-import requests
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import Qdrant
-from langchain.chains import RetrievalQA
-from langchain.agents import Tool, Agent, AgentType
-from langchain.agents import AgentExecutor
-from langchain_core.tools import Tool
-from langchain_openai import ChatOpenAI
-from langchain_openai import AzureChatOpenAI
-from langchain_community.document_loaders import JSONLoader
-
-embeddings = OpenAIEmbeddings()
-llm_1 = AzureChatOpenAI(openai_api_version=os.environ.get("AZURE_OPENAI_VERSION", "2023-07-01-preview"),
-        azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt4chat"),
-        azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT", "https://gpt-4-trails.openai.azure.com/"),
-        api_key=os.environ.get("AZURE_OPENAI_KEY"))
-
-llm = ChatOpenAI(temperature=0.2,
-                 model="gpt-3.5-turbo-0125",
-                 streaming=True,
-                 callbacks=[FinalStreamingStdOutCallbackHandler()]).configurable_fields(
-    temperature=ConfigurableField(
-        id="llm_temperature",
-        name="LLM Temperature",
-        description="The temperature of the LLM"))
-assistant_system_message = """You are a helpful assistant. \
-Use tools (only if necessary) to best answer the users questions."""
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", assistant_system_message),
-        MessagesPlaceholder(variable_name="chat_history"),
-        ("user", "{input}"),
-        MessagesPlaceholder(variable_name="agent_scratchpad"),
-    ]
-)
-
-# Define the API call function for Ares API
-
-def api_call(text):
-  url = "https://api-ares.traversaal.ai/live/predict"
-
-  payload = { "query": [text]}
-  headers = {
-    "x-api-key": "ares_a0866ad7d71d2e895c5e05dce656704a9e29ad37860912ad6a45a4e3e6c399b5",
-    "content-type": "application/json"
-  }
-
-  response = requests.post(url, json=payload, headers=headers)
-
-  # here we will use the llm to summarize the response received from the ares api
-  response_data = response.json()
-  #print(response_data)
-  try:
-    response_text = response_data['data']['response_text']
-    web_urls = response_data['data']['web_url']
-    # Continue processing the data...
-  except KeyError:
-    print("Error: Unexpected response from the API. Please try again or contact the api owner.")
-    # Optionally, you can log the error or perform other error handling actions.
-  
-
-  if len(response_text) > 10000:
-    response_text = response_text[:8000]
-    prompt = f"Summarize the following text in 500-100 0 words and jsut summarize what you see and do not add anythhing else: {response_text}"
-    summary = llm_1.invoke(prompt)
-    print(summary)
-  else:
-    summary = response_text
-
-  result = "{} My list is: {}".format(response_text, web_urls)
-
-# Convert the result to a string
-  result_str = str(result)
-
-  return result_str
-
-
-
-def metadata_func(record: str, metadata: dict) -> dict:
-    lines = record.split('\n')
-    locality_line = lines[10]
-    price_range_line = lines[12]
-    locality = locality_line.split(': ')[1]
-    price_range = price_range_line.split(': ')[1]
-    metadata["location"] = locality
-    metadata["price_range"] = price_range
-
-    return metadata
-
-# Instantiate the JSONLoader with the metadata_func
-jq_schema = '.parser[] | to_entries | map("\(.key): \(.value)") | join("\n")'
-loader = JSONLoader(
-    jq_schema=jq_schema,
-    file_path='data.json',
-    metadata_func=metadata_func,
-)
-
-# Load the JSON file and extract metadata
-documents = loader.load()
-
-    
-from langchain.vectorstores import FAISS
-def get_vectorstore(text_chunks):
-    # Check if the FAISS index file already exists
-    if os.path.exists("faiss_index"):
-        # Load the existing FAISS index
-        vectorstore = FAISS.load_local("faiss_index")
-        print("Loaded existing FAISS index.")
-    else:
-        # Create a new FAISS index
-        embeddings = OpenAIEmbeddings()
-        vectorstore = FAISS.from_documents(documents=text_chunks, embedding=embeddings)
-        # Save the new FAISS index locally
-        vectorstore.save_local("faiss_index")
-        print("Created and saved new FAISS index.")
-    return vectorstore
-
-#docs = new_db.similarity_search(query)
-
-vector = get_vectorstore(documents)
-
-template = """
-
-context:- I have low budget what is the best hotel in Instanbul?
-anser:- The other hotels in instanbul are costly and are not in your budget. so the best hotel in instanbul for you is hotel is xyz."
-
-Don’t give information not mentioned in the CONTEXT INFORMATION. 
-The system should take into account various factors such as location, amenities, user reviews, and other relevant criteria to 
-generate informative and personalized explanations.
-{context} 
-Question: {question}
-Answer:"""
-
-def search():
-    #llm = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0)
-    vector = vector
-    prompt = PromptTemplate(template=template, input_variables=["context","question"])
-    chain_type_kwargs = {"prompt": prompt}
-    return RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=vector.as_retriever(),
-    chain_type_kwargs=chain_type_kwargs,
-)
-
-# Initialize LangChain tools
-
-
-api_tool = Tool(name="Ares_API",
-                func=api_call,
-                description="Integration with Traversaal AI Ares API for real-time internet searches."
-               )
-
-chain_rag_tool = Tool(name="RAG_Chain",
-                      func=search,
-                      description="RAG chain for question answering."
-                     )
-
-app = FastAPI(
-    title='Example',
-)
-
-tools = [chain_rag_tool, api_tool]
-llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
-
-
-def _format_chat_history(chat_history: List[Tuple[str, str]]):
-    buffer = []
-    for human, ai in chat_history:
-        buffer.append(HumanMessage(content=human))
-        buffer.append(AIMessage(content=ai))
-    return buffer
-
-
-agent = (
-    {
-        "input": lambda x: x["input"],
-        "chat_history": lambda x: _format_chat_history(x["chat_history"]),
-        "agent_scratchpad": lambda x: format_to_openai_function_messages(
-            x["intermediate_steps"]
-        ),
-    }
-    | prompt
-    | llm_with_tools
-    | OpenAIFunctionsAgentOutputParser()
-)
-
-
-class AgentInput(BaseModel):
-    input: str
-    chat_history: List[Tuple[str, str]] = Field(
-        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
-    )
-
-
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
-    input_type=AgentInput
-)
-
-
-def get_response(user_input):
-    response = agent_executor.invoke({"input":user_input, "chat_history": _format_chat_history([])})
-    return response
-
-
-def main():
-    st.title("Travle Assistant Chatbot")
-    st.write("Welcome to the Hotel Assistant Chatbot!")
-    user_input = st.text_input("User Input:")
-    
-    if st.button("Submit"):
-        response = get_response(user_input)
-        st.text_area("Chatbot Response:", value=response)
-    
-    if st.button("Exit"):
-        st.stop()
-
-if __name__ == "__main__":
-    main()