Spaces:

Dorcatz123
/

Cancer_Researcher_AI

Sleeping

File size: 12,513 Bytes

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()