File size: 2,705 Bytes
9dcb51d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# In[16]:
import os
import gradio as gr
from swarmauri.standard.llms.concrete.GroqModel import GroqModel
from swarmauri.standard.conversations.concrete.MaxSystemContextConversation import MaxSystemContextConversation
from swarmauri.standard.conversations.concrete.Conversation import Conversation
from swarmauri.standard.vector_stores.concrete.TfidfVectorStore import TfidfVectorStore
from swarmauri.standard.messages.concrete.SystemMessage import SystemMessage
from swarmauri.standard.messages.concrete.HumanMessage import HumanMessage
from swarmauri.standard.documents.concrete.Document import Document
from swarmauri.standard.agents.concrete.RagAgent import RagAgent
from swarmauri.standard.agents.concrete.SimpleConversationAgent import SimpleConversationAgent
os.environ['GROQ_API_KEY'] = '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'
llm = GroqModel(api_key=os.environ['GROQ_API_KEY'])
# In[17]:
def extract_text_from_pdf(pdf_file_path):
import fitz
document = fitz.open(pdf_file_path)
text = ""
for page_num in range(document.page_count):
page = document[page_num]
text += page.get_text()
document.close()
return text
# In[25]:
def process_pdf(input_text, history, pdf_file):
if pdf_file:
temp_path = pdf_file.name
text = extract_text_from_pdf(temp_path)
system_context = SystemMessage(content='You are a RAG assistant that gives information about the pdf uploaded.')
conversation = MaxSystemContextConversation(system_context=system_context)
vector_store = TfidfVectorStore()
documents = [Document(content=text)]
vector_store.add_documents(documents)
agent = RagAgent(
llm=llm,
conversation=conversation,
system_context=system_context,
vector_store=vector_store
)
else:
# system_context = SystemMessage(content='You are a good conversation partner.')
conversation = Conversation()
initial_user_message = HumanMessage(content="Hello")
conversation.add_message(initial_user_message)
agent = SimpleConversationAgent(
llm=llm,
conversation=conversation
)
response = agent.exec(input_text)
return str(response)
# In[26]:
def main():
iface = gr.ChatInterface(
fn=process_pdf,
additional_inputs=gr.File(label="Upload PDF"),
title="PDF Document Analyzer",
description="Upload a PDF file to extract text and analyze it."
)
iface.launch(share=True)
if __name__ == "__main__":
main()
# In[ ]:
# In[ ]:
|