Duplicate from BHO/URDtest

.gitattributes

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README.md

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+---
+title: URDtest
+emoji: 😻
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 3.24.1
+app_file: app.py
+pinned: false
+license: openrail
+duplicated_from: BHO/URDtest
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

README1.txt

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+Test2

app.py

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+import gradio as gr
+import os 
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import DirectoryLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+
+
+# Set the path of your new directory
+dir_path = "./docs"
+
+# Create the directory using the os module
+os.makedirs(dir_path, exist_ok=True)
+
+# Print a confirmation message
+print(f"New directory created at {dir_path}")
+
+def qa_system(pdf_file, openai_key, prompt, chain_type, k):
+    os.environ["OPENAI_API_KEY"] = openai_key
+    
+    # load document
+    # loader = PyPDFLoader(pdf_file.name)
+    loader = DirectoryLoader(dir_path, glob="**/*.pdf") #, loader_cls=PDFLoader)
+    documents = loader.load()
+    
+    # split the documents into chunks
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    
+    # select which embeddings we want to use
+    embeddings = OpenAIEmbeddings()
+    
+    # create the vectorestore to use as the index
+    db = Chroma.from_documents(texts, embeddings)
+    
+    # expose this index in a retriever interface
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
+    
+    # create a chain to answer questions 
+    qa = RetrievalQA.from_chain_type(
+        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)
+    
+    # get the result
+    result = qa({"query": prompt})
+    return result['result'], [doc.page_content for doc in result["source_documents"]]
+
+# define the Gradio interface
+input_file = gr.inputs.File(label="PDF File")
+openai_key = gr.inputs.Textbox(label="OpenAI API Key", type="password")
+prompt = gr.inputs.Textbox(label="Question Prompt")
+chain_type = gr.inputs.Radio(['stuff', 'map_reduce', "refine", "map_rerank"], label="Chain Type")
+k = gr.inputs.Slider(minimum=1, maximum=5, default=1, label="Number of Relevant Chunks")
+
+output_text = gr.outputs.Textbox(label="Answer")
+output_docs = gr.outputs.Textbox(label="Relevant Source Text")
+
+gr.Interface(qa_system, inputs=[input_file, openai_key, prompt, chain_type, k], outputs=[output_text, output_docs], 
+             title="Question Answering with PDF File and OpenAI", 
+             description="Upload a PDF file, enter your OpenAI API key, type a question prompt, select a chain type, and choose the number of relevant chunks to use for the answer.").launch(debug = True)
+

docs/TC - URD 2019 EN mise en ligne.pdf

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requirements.txt

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+pdfminer.six
+unstructured
+langchain
+openai
+chromadb
+pypdf
+tiktoken
+panel
+gradio