Update app.py
Browse files
app.py
CHANGED
@@ -7,7 +7,7 @@ from langchain.vectorstores import FAISS
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from langchain.chains import ConversationalRetrievalChain
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import ConversationBufferMemory
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openai_api_key = os.environ.get("OPENAI_API_KEY")
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class AdvancedPdfChatbot:
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@@ -18,12 +18,17 @@ class AdvancedPdfChatbot:
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self.llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
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self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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self.qa_chain = None
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def load_and_process_pdf(self, pdf_path):
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loader = PyPDFLoader(pdf_path)
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documents = loader.load()
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texts = self.text_splitter.split_documents(documents)
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self.db
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self.setup_conversation_chain()
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def setup_conversation_chain(self):
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@@ -40,14 +45,15 @@ class AdvancedPdfChatbot:
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return result['answer']
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# Initialize the chatbot
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pdf_chatbot = AdvancedPdfChatbot(openai_api_key)
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def upload_pdf(pdf_file):
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if pdf_file is None:
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return "Please upload a PDF file."
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file_path = pdf_file.name
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pdf_chatbot.load_and_process_pdf(file_path)
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return "PDF uploaded and processed successfully. You can now start chatting!"
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def respond(message, history):
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@@ -58,7 +64,7 @@ def respond(message, history):
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# Create the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("# PDF Chatbot")
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with gr.Row():
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pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
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upload_button = gr.Button("Process PDF")
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@@ -74,4 +80,4 @@ with gr.Blocks() as demo:
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clear.click(lambda: None, None, chatbot_interface, queue=False)
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if __name__ == "__main__":
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demo.launch()
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from langchain.chains import ConversationalRetrievalChain
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import ConversationBufferMemory
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+
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openai_api_key = os.environ.get("OPENAI_API_KEY")
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class AdvancedPdfChatbot:
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self.llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
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self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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self.qa_chain = None
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self.db = None
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def load_and_process_pdf(self, pdf_path):
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loader = PyPDFLoader(pdf_path)
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documents = loader.load()
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texts = self.text_splitter.split_documents(documents)
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if self.db is None:
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self.db = FAISS.from_documents(texts, self.embeddings)
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else:
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new_db = FAISS.from_documents(texts, self.embeddings)
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self.db.merge_from(new_db)
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self.setup_conversation_chain()
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def setup_conversation_chain(self):
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return result['answer']
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# Initialize the chatbot
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pdf_chatbot = AdvancedPdfChatbot(openai_api_key)
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def upload_pdf(pdf_file):
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if pdf_file is None:
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return "Please upload a PDF file."
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file_path = pdf_file.name
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pdf_file.save(file_path) # Ensure the file is saved locally
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pdf_chatbot.load_and_process_pdf(file_path)
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os.remove(file_path) # Clean up the uploaded file after processing
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return "PDF uploaded and processed successfully. You can now start chatting!"
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def respond(message, history):
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# Create the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("# PDF Chatbot")
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with gr.Row():
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pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
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upload_button = gr.Button("Process PDF")
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clear.click(lambda: None, None, chatbot_interface, queue=False)
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if __name__ == "__main__":
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demo.launch()
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