import os

import gradio as gr

from embedchain import App
from embedchain.config.vectordb.pinecone import PineconeDBConfig

import os


#Initializing the Embedchain application with open source configuration
config = {
  'llm': {
    'provider': 'huggingface',
    'config': {
      'model': 'mistralai/Mistral-7B-Instruct-v0.2',
      'top_p': 0.5
    }
  },
  'embedder': {
    'provider': 'huggingface',
    'config': {
      'model': 'sentence-transformers/all-mpnet-base-v2'
    },
  },
   'vectordb': {
        'provider': 'pinecone',
        'config': {
            'metric': 'cosine',
            'vector_dimension': 768,
            'index_name': 'vox'
        }
    }
  }

app = App.from_config(config=config)
# # Add data to the app
# app.add("https://voxiitk.com/scrapping-of-galaxy24/")
# app.add("https://voxiitk.com/administration-responds-to-students-senates-demands/")
# app.add("https://voxiitk.com/yeh-hall-ab-13-na-raha/")
# app.add("https://voxiitk.com/girls-protest-at-directors-residence/")
# app.add("https://www.iitk.ac.in/doaa/data/UG-Manual.pdf")
# app.add("student_data.student_data.json")

def query(message, history):
  answer = app.query(message)
  start_index = answer.find("Answer:") + 7
  output = answer[start_index:]
  
  return output

demo = gr.ChatInterface(query)

demo.launch()