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import requests
from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, ServiceContext, PromptHelper
import gradio as gr
import os
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Get Hugging Face API key from environment variable
hf_api_key = os.getenv("HF_API_KEY")
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
headers = {"Authorization": f"Bearer {hf_api_key}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
def init_index(directory_path):
# Model parameters
max_input_size = 4096
num_outputs = 512
max_chunk_overlap = 20
chunk_size_limit = 600
# Prompt helper and predictor
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
llm_predictor = LLMPredictor(llm=query)
# Read documents from the "docs" folder
documents = SimpleDirectoryReader(directory_path).load_data()
# Initialize index with document data
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
# Save the created index
index.save_to_disk('index.json')
return index
def chatbot(input_text):
# Load index
index = GPTSimpleVectorIndex.load_from_disk('index.json')
# Get response for the question
response = index.query(input_text, response_mode="compact")
return response.response
# Create index
init_index("docs")
# Create UI interface to interact with the Hugging Face model
iface = gr.Interface(fn=chatbot,
inputs=gr.components.Textbox(lines=7, placeholder="Enter your question here"),
outputs="text",
title="Frost AI ChatBot: Your Knowledge Companion Powered by Hugging Face",
description="Ask any question about rahasak research papers",
allow_screenshot=True)
iface.launch(share=True)
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