Upload app.py

updated the gradio inputs

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-import os 
+import os
 from langchain.chains import RetrievalQA
 from langchain.llms import OpenAI
 from langchain.document_loaders import PyPDFLoader
@@ -7,55 +7,48 @@ from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import Chroma
 
-def qa_system(pdf_file, openai_key, prompt, chain_type, k):
+def qa_system(pdf_files, openai_key, prompt, chain_type , k):
     os.environ["OPENAI_API_KEY"] = openai_key
-    
-    # load document
-    loader = PyPDFLoader(pdf_file.name)
-    documents = loader.load()
-    
-    # split the documents into chunks
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    texts = text_splitter.split_documents(documents)
-    
+
+    texts = []
+
+    # load documents from PDF files
+    for pdf_file in pdf_files:
+        loader = PyPDFLoader(pdf_file.name)
+        documents = loader.load()
+
+        # split the documents into chunks
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        texts.extend(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 
+
+    # 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'], ''.join(doc.page_content for doc in result["source_documents"])
+    return result['result'], ''.join(doc.page_content for doc in rest["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"],default = 'map_reduce',label="Chain Type")
-k = gr.inputs.Slider(minimum=1, maximum=5, default=2, label="Number of Relevant Chunks")
+input_file = gr.File(file_count="multiple",label="PDF File")
+openai_key = gr.Textbox(label="OpenAI API Key", type="password")
+prompt = gr.Textbox(label="Question Prompt")
+chain_type = gr.Radio(['stuff', 'map_reduce', "refine", "map_rerank"], label="Chain Type",default = 'map_reduce')
+k = gr.Slider(minimum=1, maximum=5, default=2, label="Number of Relevant Chunks")
 
-output_text = gr.outputs.Textbox(label="Answer")
-output_docs = gr.outputs.Textbox(label="Relevant Source Text")
+output_text = gr.Textbox(label="Answer")
+output_docs = gr.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", 
+gr.Interface(qa_system, inputs=[input_file, openai_key, prompt, chain_type, k], outputs=[output_text, output_docs],
+             title="DocuAI",
              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)
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