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import spaces
import os
import openai
from dotenv import load_dotenv
_ = load_dotenv() # read local .env file
import gradio as gr
from langchain_chroma import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from spaces import GPU # Import GPU decorator
# Custom class to handle API routing for different models
class ChatOpenRouter(ChatOpenAI):
openai_api_base: str
openai_api_key: str
model_name: str
def __init__(self, model_name: str, openai_api_key: str = None, openai_api_base: str = "https://openrouter.ai/api/v1", **kwargs):
openai_api_key = openai_api_key or os.getenv('OPENROUTER_API_KEY')
super().__init__(openai_api_base=openai_api_base, openai_api_key=openai_api_key, model_name=model_name, **kwargs)
# Initialize embedding function here
embedding_function = OpenAIEmbeddings()
# Updated cbfs class with dynamic database and model selection
@GPU # Ensure this class runs on the GPU
class cbfs:
def __init__(self, persist_directory, model_name):
self.chat_history = []
self.answer = ""
self.db_query = ""
self.db_response = []
self.panels = []
# Initialize Chroma and the ConversationalRetrievalChain with the chosen database and model
db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
# Select model dynamically
if model_name == "GPT-4":
chosen_llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0)
elif model_name == "GPT-3.5":
chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo-0125", temperature=0)
elif model_name == "Llama-3 8B":
chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-8b-instruct", temperature=0)
elif model_name == "Gemini-1.5 Pro":
chosen_llm = ChatOpenRouter(model_name="google/gemini-pro-1.5", temperature=0)
elif model_name == "Claude 3 Sonnet":
chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3-sonnet', temperature=0)
elif model_name == "Claude 3.5 Sonnet":
chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3.5-sonnet', temperature=0)
else:
# Default model
chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-70b-instruct", temperature=0)
self.qa = ConversationalRetrievalChain.from_llm(
llm=chosen_llm,
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
)
@spaces.GPU
def convchain(self, query):
if not query:
return [("User", ""), ("ChatBot", "")]
result = self.qa.invoke({"question": query, "chat_history": self.chat_history})
self.chat_history.append((query, result["answer"]))
self.db_query = result["generated_question"]
self.db_response = result["source_documents"]
self.answer = result['answer']
self.panels.append(["User", query]) # Ensure this is a list of two strings
self.panels.append(["ChatBot", self.answer]) # Ensure this is a list of two strings
return self.panels
def clr_history(self):
self.chat_history = []
self.panels = []
return self.panels # Clear the chatbot display
# Create Gradio interface functions
cbfs_instances = {} # Dictionary to store instances based on db_choice and model_choice
def initialize_cbfs(db_choice, model_choice):
"""Initialize cbfs object based on the database and model selection and clear history."""
key = (db_choice, model_choice)
if key not in cbfs_instances:
if db_choice == "Governance Documents":
cbfs_instances[key] = cbfs(persist_directory='docs/chroma_eg/', model_name=model_choice)
elif db_choice == "Faculty Handbook":
cbfs_instances[key] = cbfs(persist_directory='docs/chroma_hb/', model_name=model_choice)
return cbfs_instances[key]
def chat_history(query, db_choice, model_choice):
"""Handles chat submissions. Reminds the user to select a document if none is selected."""
cb = initialize_cbfs(db_choice, model_choice)
if not cb: # If cb is not initialized, remind to select a document
return [("ChatBot", "Please select a document from the dropdown menu before submitting your query.")], ""
else:
return cb.convchain(query), "" # Clear input box by returning empty string
def clear_history(db_choice, model_choice):
cb = initialize_cbfs(db_choice, model_choice)
cb.clr_history()
return [], ""
# Create Gradio UI layout
with gr.Blocks() as demo:
# Full-width image at the top
with gr.Row():
gr.Image("isu_logo.jpg", elem_id="full_width_image", show_label=False)
# Full-width text below the image
with gr.Row():
gr.Markdown("<h1 style='text-align: center; font-size: 3.5em;'>Department of Economics</h1>")
gr.Markdown("# Faculty Policies & Rules ChatBot")
with gr.Row():
db_choice = gr.Dropdown(["Governance Documents", "Faculty Handbook"], label="Select Document", scale=1)
model_choice = gr.Dropdown(["GPT-3.5", "GPT-4", "Llama-3 70B", "Llama-3 8B", "Gemini-1.5 Pro", "Claude 3 Sonnet", "Claude 3.5 Sonnet"],
label="Select Model", scale=1, value="Llama-3 70B")
button_clearhistory = gr.Button("Clear History", scale=1)
with gr.Row():
inp = gr.Textbox(placeholder="Enter text here…", scale=8)
button_submit = gr.Button("Submit", scale=1)
output = gr.Chatbot()
# Update cbfs_instance and clear chat history when the dropdown values change
db_choice.change(
fn=clear_history,
inputs=[db_choice, model_choice],
outputs=[output, inp]
)
model_choice.change(
fn=clear_history,
inputs=[db_choice, model_choice],
outputs=[output, inp]
)
# Define interactions for both submit button and Enter key
inp.submit(fn=chat_history, inputs=[inp, db_choice, model_choice], outputs=[output, inp])
button_submit.click(fn=chat_history, inputs=[inp, db_choice, model_choice], outputs=[output, inp])
button_clearhistory.click(fn=clear_history, inputs=[db_choice, model_choice], outputs=[output, inp])
# Launch the Gradio app
demo.launch()
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