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| import gradio as gr | |
| import os | |
| from langchain.chains import create_history_aware_retriever, create_retrieval_chain | |
| from langchain.chains.combine_documents import create_stuff_documents_chain | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.chat_message_histories import ChatMessageHistory | |
| from langchain_community.document_loaders import WebBaseLoader | |
| from langchain_core.chat_history import BaseChatMessageHistory | |
| from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder | |
| from langchain_core.runnables.history import RunnableWithMessageHistory | |
| from langchain_openai import ChatOpenAI, OpenAIEmbeddings | |
| from langchain_text_splitters import RecursiveCharacterTextSplitter | |
| import pandas as pd | |
| from getpass import getpass | |
| from huggingface_hub import hf_hub_download | |
| from huggingface_hub import hf_hub_download | |
| import os | |
| import faiss | |
| def interactive_chat(question, openai_api_key, state=None): | |
| os.environ["OPENAI_API_KEY"]=openai_api_key | |
| llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) | |
| embeddings = OpenAIEmbeddings() | |
| # Download the actual FAISS index file inside the folder | |
| faiss_index_path = hf_hub_download( | |
| repo_id="DataScienceAcademy/Cancer_Researcher_AI", | |
| filename="faiss_index/index.faiss", # Path to the actual file | |
| repo_type="space", | |
| use_auth_token=True # If private, authenticate | |
| ) | |
| # Load the FAISS index from the downloaded file | |
| index = faiss.read_index(faiss_index_path) | |
| # Then call the function to handle the OpenAI key setup | |
| dir_path = os.path.dirname(os.path.realpath(__file__)) | |
| csv_file_path = os.path.join(dir_path, 'Final_Stacked_Data_without_Duplicates.csv') | |
| df = pd.read_csv(csv_file_path) | |
| df = df.drop(['Unnamed: 0'], axis=1) | |
| # Combine relevant columns of df 'Title', 'Authors', 'Published', 'Journal', 'Abstract', 'Link' into a single row | |
| df['combined'] = df.apply( | |
| lambda row: f"Title: {row['Title']}\n" | |
| f"Authors: {row['Authors']}\n" | |
| f"Abstract: {row['Abstract']}\n" | |
| f"Link: {row['Link']}\n", | |
| axis=1 | |
| ) | |
| # Strip spaces | |
| df['combined'] = df['combined'].str.strip() | |
| # Check the combined data | |
| # print("Combined Data Example:\n", df['combined'].head()) | |
| # Ask for the OpenAI API key if not already set | |
| print(''' | |
| .d8888b. 8888888b. 888 d8888 8888888 | |
| d88P Y88b 888 Y88b 888 d88888 888 | |
| 888 888 888 888 888 d88P888 888 | |
| 888 8888b. 88888b. .d8888b .d88b. 888d888 888 d88P .d88b. .d8888b .d88b. 8888b. 888d888 .d8888b 88888b. .d88b. 888d888 d88P 888 888 | |
| 888 "88b 888 "88b d88P" d8P Y8b 888P" 8888888P" d8P Y8b 88K d8P Y8b "88b 888P" d88P" 888 "88b d8P Y8b 888P" d88P 888 888 | |
| 888 888 .d888888 888 888 888 88888888 888 888 T88b 88888888 "Y8888b. 88888888 .d888888 888 888 888 888 88888888 888 d88P 888 888 | |
| Y88b d88P 888 888 888 888 Y88b. Y8b. 888 888 T88b Y8b. X88 Y8b. 888 888 888 Y88b. 888 888 Y8b. 888 d8888888888 888 | |
| "Y8888P" "Y888888 888 888 "Y8888P "Y8888 888 888 T88b "Y8888 88888P' "Y8888 "Y888888 888 "Y8888P 888 888 "Y8888 888 d88P 888 8888888 ''') | |
| print("\n\n Hi there! Thank you for reaching out to me!\n\n") | |
| # Convert the combined text into a list | |
| documents = df['combined'].tolist() | |
| # Create FAISS vector store with the embeddings | |
| if os.path.exists(f"{dir_path}/faiss_index"): | |
| vector_store = FAISS.load_local(f"{dir_path}/faiss_index", embeddings, allow_dangerous_deserialization=True) | |
| retriever = vector_store.as_retriever(search_kwargs={"k": 5}) | |
| else: | |
| vector_store = FAISS.from_texts(documents, embeddings) | |
| vector_store.save_local(f"{dir_path}/faiss_index") | |
| retriever = vector_store.as_retriever(search_kwargs={"k": 5}) | |
| # Contextualize question | |
| contextualize_q_system_prompt = ( | |
| "Given a chat history and the latest user question " | |
| "which might reference context in the chat history, " | |
| "formulate a standalone question which can be understood " | |
| "without the chat history. Do NOT answer the question, " | |
| "just reformulate it if needed and otherwise return it as is." | |
| ) | |
| contextualize_q_prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", contextualize_q_system_prompt), | |
| MessagesPlaceholder("chat_history"), | |
| ("human", "{input}"), | |
| ] | |
| ) | |
| history_aware_retriever = create_history_aware_retriever( | |
| llm, retriever, contextualize_q_prompt | |
| ) | |
| # Answer question | |
| system_prompt = ( | |
| '''You are a cancer research assistant. When the user asks about a specific type of cancer (e.g., brain tumor), you should: | |
| 1. First explain the concept or type of cancer briefly. | |
| 2. Provide at least 5 relevant links to papers related to that cancer from the available dataset. If not, provide however many there are. | |
| Question: {input} | |
| Context: {context} | |
| Output: | |
| 1. Brief Explanation: | |
| 2. Relevant Research Papers (with links): | |
| If the user asks for summaries or explanations of papers that you provided, you should look at the list of papers you provided for the previous question and summarize what is required. | |
| If the user's question is not related to cancer, do not try to find similarities, just say, "I don't know." | |
| ''' | |
| ) | |
| qa_prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", system_prompt), | |
| MessagesPlaceholder("chat_history"), | |
| ("human", "{input}"), | |
| ] | |
| ) | |
| question_answer_chain = create_stuff_documents_chain(llm, qa_prompt) | |
| rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain) | |
| # Statefully manage chat history | |
| store = {} | |
| def get_session_history(session_id: str) -> BaseChatMessageHistory: | |
| if session_id not in store: | |
| store[session_id] = ChatMessageHistory() | |
| return store[session_id] | |
| conversational_rag_chain = RunnableWithMessageHistory( | |
| rag_chain, | |
| get_session_history, | |
| input_messages_key="input", | |
| history_messages_key="chat_history", | |
| output_messages_key="answer", | |
| ) | |
| def ask_cancer_question(question, session_id="user_session"): | |
| if question.lower() == "exit": | |
| return "Exiting the interactive question loop." | |
| response = conversational_rag_chain.invoke( | |
| {"input": question}, | |
| config={"configurable": {"session_id": session_id}} | |
| )["answer"] | |
| return response | |
| if state is None: | |
| state = [] # Initialize the state (chat history) if it's the first interaction | |
| if question.lower() == "exit": | |
| return "\n".join(state) + "\nExiting the interactive question loop.", state | |
| # Here, use the ask_cancer_question function (that you defined earlier) | |
| response = ask_cancer_question(question) # Or define your chatbot logic here | |
| state.append(f"{response}") | |
| return "\n".join(state) | |
| # 🎨 Gradio UI | |
| # demo = gr.Interface( | |
| # fn=interactive_chat, | |
| # inputs=[ | |
| # gr.Textbox(lines=2, placeholder="Ask a cancer-related question...", elem_id="query_input"), | |
| # gr.Textbox(lines=2, placeholder="openai_api_key", type="password", elem_id="openai_key") | |
| # ], | |
| # outputs=gr.Textbox(lines=10, elem_id="Output"), | |
| # title="🧬 Cancer Research Chatbot", | |
| # description="Ask cancer-related questions and receive responses based on relevant research." | |
| # ) | |
| # title="Cancer_Researcher_AI", | |
| # # Custom CSS to style the input boxes | |
| # demo.css = """ | |
| # #query_input { | |
| # border: 2px solid #4CAF50; /* Green border for a medical feel */ | |
| # border-radius: 8px; /* Rounded corners */ | |
| # padding: 12px; /* Slightly more padding */ | |
| # margin-bottom: 20px; /* Space below the input box */ | |
| # transition: border-color 0.3s ease, background-color 0.3s ease; /* Smooth transition */ | |
| # background-color: #E8F5E9; /* Light green background for a soothing medical look */ | |
| # font-size: 14px; /* Slightly smaller font for input */ | |
| # } | |
| # #query_input:focus { | |
| # border-color: #66BB6A; /* Darker green when focused */ | |
| # background-color: #C8E6C9; /* Slightly darker green background on focus */ | |
| # } | |
| # #query_inputs { | |
| # background-color: #E8F5E9; /* Light green background */ | |
| # } | |
| # #openai_key { | |
| # background-color: #E8F5E9; | |
| # } | |
| # #Output { | |
| # border: 2px solid #2196F3; /* Blue border to indicate information */ | |
| # border-radius: 8px; /* Rounded corners */ | |
| # padding: 15px; /* Added padding inside the output box */ | |
| # background-color: #E3F2FD; /* Light blue background for readability */ | |
| # margin-top: 20px; /* Space above the output box */ | |
| # font-size: 16px; /* Font size for readability */ | |
| # font-family: Arial, sans-serif; /* Use a clean font for scientific feel */ | |
| # color: #1E88E5; /* Blue text for contrast */ | |
| # line-height: 1.5; /* Improve readability */ | |
| # } | |
| # /* Optional: Adding some styling for the chatbot header (title) */ | |
| # #title { | |
| # font-size: 22px; | |
| # font-weight: bold; | |
| # color: #1E88E5; /* Blue for a professional, clean look */ | |
| # margin-bottom: 15px; | |
| # } | |
| # """ | |
| import gradio as gr | |
| # 🎨 Gradio UI | |
| demo = gr.Interface( | |
| fn=interactive_chat, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Ask a cancer-related question...", elem_id="query_input"), | |
| gr.Textbox(lines=1, placeholder="Enter OpenAI API key", type="password", elem_id="openai_key") | |
| ], | |
| outputs=gr.Textbox(lines=10, elem_id="Output"), | |
| title="🧬 Cancer Research Chatbot", | |
| description="Ask cancer-related questions and receive responses based on relevant research." | |
| ) | |
| # Custom CSS to style the input boxes | |
| demo.css = """ | |
| #query_input { | |
| border: 2px solid #4CAF50; | |
| border-radius: 8px; | |
| padding: 12px; | |
| margin-bottom: 20px; | |
| transition: border-color 0.3s ease, background-color 0.3s ease; | |
| background-color: #E8F5E9; | |
| font-size: 14px; | |
| } | |
| #query_input:focus { | |
| border-color: #66BB6A; | |
| background-color: #C8E6C9; | |
| } | |
| #openai_key { | |
| background-color: #E8F5E9; | |
| border: 2px solid #FF9800; /* Orange border to indicate API key importance */ | |
| border-radius: 8px; | |
| padding: 10px; | |
| } | |
| #Output { | |
| border: 2px solid #2196F3; | |
| border-radius: 8px; | |
| padding: 15px; | |
| background-color: #E3F2FD; | |
| margin-top: 20px; | |
| font-size: 16px; | |
| font-family: Arial, sans-serif; | |
| color: #1E88E5; | |
| line-height: 1.5; | |
| } | |
| #title { | |
| font-size: 22px; | |
| font-weight: bold; | |
| color: #1E88E5; | |
| margin-bottom: 15px; | |
| } | |
| """ | |
| demo.launch() | |
| # Launch the app | |
| if __name__ == '__main__': | |
| demo.launch() | |