Update app.py
Browse files
app.py
CHANGED
@@ -1,25 +1,24 @@
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import PyPDF2
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import os
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from bs4 import BeautifulSoup
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import tempfile
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import csv
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import json
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import xml.etree.ElementTree as ET
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import docx
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import pptx
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import openpyxl
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import re
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import nltk
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import time
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import requests
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import gradio as gr
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from nltk.tokenize import word_tokenize
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from langchain_community.embeddings import SentenceTransformerEmbeddings
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from langchain_community.vectorstores import FAISS
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from langchain_community.llms import HuggingFaceEndpoint
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from langchain.schema import SystemMessage, HumanMessage, AIMessage
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from langchain_community.chat_models.huggingface import ChatHuggingFace
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from youtube_transcript_api import YouTubeTranscriptApi
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from youtube_transcript_api._errors import NoTranscriptFound, TranscriptsDisabled, VideoUnavailable
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nltk.download('punkt')
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nltk.download('omw-1.4')
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@@ -203,7 +202,7 @@ def Chat_Message(history):
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message=HumanMessage(content=history[-1][0])
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messages.append(message)
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response = chat_model.invoke(messages)
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messages.append(response.content)
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if len(messages) >= 8:
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messages = messages[-8:]
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@@ -239,7 +238,7 @@ def Web_Search(history):
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msg==HumanMessage(content=augmented_prompt)
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messages.append(msg)
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response = chat_model.invoke(msg)
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messages.append(response.content)
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if len(messages) >= 8:
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messages = messages[-8:]
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@@ -269,7 +268,7 @@ def Chart_Generator(history):
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messages.append(prompt)
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res = chat_model.invoke(messages)
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messages.append(
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if len(messages) >= 8:
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messages = messages[-8:]
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@@ -322,7 +321,7 @@ def Link_Scratch(history):
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message = HumanMessage(content=augmented_prompt)
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messages.append(message)
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response = chat_model.invoke(messages)
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messages.append(response.content)
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if len(messages) >= 1:
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messages = messages[-1:]
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@@ -381,7 +380,7 @@ def File_Interact(history,filepath):
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message = HumanMessage(content=augmented_prompt)
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messages.append(message)
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response = chat_model.invoke(messages)
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messages.append(response.content)
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if len(messages) >= 1:
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messages = messages[-1:]
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import os
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import csv
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import json
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import docx
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import pptx
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import re
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import nltk
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import time
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import PyPDF2
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import tempfile
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import openpyxl
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import requests
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import gradio as gr
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from bs4 import BeautifulSoup
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import xml.etree.ElementTree as ET
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from nltk.tokenize import word_tokenize
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from langchain_community.vectorstores import FAISS
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from youtube_transcript_api import YouTubeTranscriptApi
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from langchain.schema import SystemMessage, HumanMessage, AIMessage
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from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
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from langchain_community.embeddings import SentenceTransformerEmbeddings
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from youtube_transcript_api._errors import NoTranscriptFound, TranscriptsDisabled, VideoUnavailable
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nltk.download('punkt')
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nltk.download('omw-1.4')
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message=HumanMessage(content=history[-1][0])
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messages.append(message)
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response = chat_model.invoke(messages)
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messages.append(AIMessage(content=response.content))
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if len(messages) >= 8:
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messages = messages[-8:]
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msg==HumanMessage(content=augmented_prompt)
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messages.append(msg)
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response = chat_model.invoke(msg)
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messages.append(AIMessage(content=response.content))
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if len(messages) >= 8:
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messages = messages[-8:]
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messages.append(prompt)
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res = chat_model.invoke(messages)
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messages.append(AIMessage(content=response.content))
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if len(messages) >= 8:
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messages = messages[-8:]
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message = HumanMessage(content=augmented_prompt)
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messages.append(message)
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response = chat_model.invoke(messages)
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messages.append(AIMessage(content=response.content))
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if len(messages) >= 1:
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messages = messages[-1:]
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message = HumanMessage(content=augmented_prompt)
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messages.append(message)
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response = chat_model.invoke(messages)
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messages.append(AIMessage(content=response.content))
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if len(messages) >= 1:
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messages = messages[-1:]
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