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Update app.py
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app.py
CHANGED
@@ -1,5 +1,3 @@
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import dotenv
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from dotenv import load_dotenv
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import os
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from PyPDF2 import PdfReader
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import docx
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@@ -11,36 +9,22 @@ from langchain_openai import OpenAI
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from langchain.callbacks import get_openai_callback
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import gradio as gr
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from aiohttp import web
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import gradio as gr
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load_dotenv()
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os.environ["OPENAI_API_KEY"] = "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"
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def read_txt(file_path):
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with open(file_path, "r") as file:
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text = file.read()
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return text
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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if filename.endswith(".pdf"):
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combined_text += read_pdf(file_path)
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elif filename.endswith(".docx"):
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combined_text += read_word(file_path)
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elif filename.endswith(".txt"):
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combined_text += read_txt(file_path)
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return combined_text
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text_file_path = '/content/lawsofpower.txt'
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user_query = read_txt(text_file_path)
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text_chunks = char_text_splitter.split_text(user_query)
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embeddings = OpenAIEmbeddings()
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@@ -49,6 +33,7 @@ docsearch = FAISS.from_texts(text_chunks, embeddings)
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llm = OpenAI()
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chain = load_qa_chain(llm, chain_type="stuff")
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async def chatbot_interface(request):
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data = await request.post()
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input_text = data.get("input_text", "")
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@@ -58,8 +43,10 @@ async def chatbot_interface(request):
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return web.Response(text=response)
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app = web.Application()
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app.router.add_post('/chatbot', chatbot_interface)
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if __name__ == "__main__":
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web.run_app(app, port=os.getenv("PORT", 8080))
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import os
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from PyPDF2 import PdfReader
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import docx
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from langchain.callbacks import get_openai_callback
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import gradio as gr
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from aiohttp import web
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# Load OpenAI API key directly
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os.environ["OPENAI_API_KEY"] = "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"
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# Function to read text from a file
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def read_txt(file_path):
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with open(file_path, "r") as file:
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text = file.read()
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return text
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# Load text from the specified file
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text_file_path = '/home/user/app/content/lawsofpower.txt'
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user_query = read_txt(text_file_path)
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# Set up text processing components
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char_text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
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text_chunks = char_text_splitter.split_text(user_query)
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embeddings = OpenAIEmbeddings()
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llm = OpenAI()
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chain = load_qa_chain(llm, chain_type="stuff")
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# Define the chatbot interface
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async def chatbot_interface(request):
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data = await request.post()
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input_text = data.get("input_text", "")
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return web.Response(text=response)
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# Set up the web application
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app = web.Application()
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app.router.add_post('/chatbot', chatbot_interface)
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# Run the web server
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if __name__ == "__main__":
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web.run_app(app, port=os.getenv("PORT", 8080))
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