Create app.py

app.py

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+import os
+from PyPDF2 import PdfReader
+import docx
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain_openai import OpenAI
+from langchain.callbacks import get_openai_callback
+import gradio as gr
+from aiohttp import web
+
+# Set your OpenAI API key
+os.environ["OPENAI_API_KEY"] = "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"
+
+# Function to read text from a file
+def read_txt(file_path):
+    with open(file_path, "r") as file:
+        text = file.read()
+    return text
+
+# Load text from the specified file
+text_file_path = '/home/user/app/content/lawsofpower.txt'
+user_query = read_txt(text_file_path)
+
+# Set up text processing components
+char_text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
+text_chunks = char_text_splitter.split_text(user_query)
+
+embeddings = OpenAIEmbeddings()
+docsearch = FAISS.from_texts(text_chunks, embeddings)
+
+llm = OpenAI()
+chain = load_qa_chain(llm, chain_type="stuff")
+
+# Define the chatbot interface
+async def chatbot_interface(request):
+    data = await request.post()
+    input_text = data.get("input_text", "")
+    
+    docs = docsearch.similarity_search(input_text)
+    response = chain.run(input_documents=docs, question=input_text)
+    
+    return web.Response(text=response)
+
+# Set up the web application
+app = web.Application()
+app.router.add_post('/chatbot', chatbot_interface)
+
+# Run the web server
+if __name__ == "__main__":
+    web.run_app(app, port=os.getenv("PORT", 8080))