Create app.py

app.py

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+import gradio as gr
+
+from langchain.document_loaders import PyPDFLoader  # for loading the pdf
+from langchain.embeddings import OpenAIEmbeddings  # for creating embeddings
+from langchain.vectorstores import Chroma  # for the vectorization part
+from langchain.chains import ChatVectorDBChain  # for chatting with the pdf
+from langchain.llms import OpenAI  # the LLM model we'll use (CHatGPT)
+
+
+class Chat:
+    def __init__(self, pdf, api_input):
+        self.api = api_input
+        loader = PyPDFLoader(pdf)
+        pages = loader.load_and_split()
+
+        embeddings = OpenAIEmbeddings(openai_api_key=self.api)
+        vectordb = Chroma.from_documents(pages, embedding=embeddings, persist_directory=".")
+        vectordb.persist()
+
+        self.pdf_qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0.9, model_name="gpt-3.5-turbo",
+                                                        openai_api_key=self.api),
+                                                 vectordb, return_source_documents=True)
+
+    def question(self, query):
+        result = self.pdf_qa({"question": "请使用中文回答" + query, "chat_history": ""})
+        print("Answer:")
+        print(result["answer"])
+
+        return result["answer"]
+
+
+def analyse(pdf_file, api_input):
+    session = Chat(pdf_file.name, api_input)
+    return session, "文章分析完成"
+
+
+def ask_question(data, question):
+    if data == "":
+        return "Please upload PDF file first!"
+    return data.question(question)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # ChatPDF based on Langchain
+        """)
+    data = gr.State()
+    with gr.Tab("Upload PDF File"):
+        pdf_input = gr.File(label="PDF File")
+        api_input = gr.Textbox(label="OpenAI API Key")
+        result = gr.Textbox()
+        upload_button = gr.Button("Start Analyse")
+        question_input = gr.Textbox(label="Your Question", placeholder="Authors of this paper?")
+        answer = gr.Textbox(label="Answer")
+        ask_button = gr.Button("Ask")
+
+    upload_button.click(fn=analyse, inputs=[pdf_input, api_input], outputs=[data, result])
+    ask_button.click(ask_question, inputs=[data, question_input], outputs=answer)
+
+if __name__ == "__main__":
+    demo.title = "ChatPDF Based on Langchain"
+    demo.launch()