Update app.py

app.py

@@ -1,244 +1,271 @@
-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
-
-# Download the faiss_index folder from Hugging Face
-faiss_index_path = hf_hub_download(
-    repo_id="Dorcatz123/Cancer_Researcher_AI",  # Your Hugging Face Space repo ID
-    filename="faiss_index",  # Folder name in the repo
-    repo_type="space",  # Set this to "space" for a Hugging Face Space
-    use_auth_token=True  # If your repo is private, this ensures authentication
-)
-
-# Load the FAISS index (Adjust path if necessary)
-index_path = os.path.join(faiss_index_path, "faiss_index")  # Path to the downloaded folder
-import faiss
-index = faiss.read_index(index_path)
-
-
-
-try:
-    if api:
-        set_key('.env', 'OPENAI_API_KEY', api)
-except Exception as e:
-    return f"❌ Error: {str(e)}"
-
-load_dotenv()
-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")
-
-# LLM
-llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
-
-# Initialize OpenAI embeddings
-embeddings = OpenAIEmbeddings()
-
-# 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",
-)
-
-
-# Interactive Loop
-# user_input = input("\nAsk a cancer-related question (or type 'exit' to quit): ")
-#
-# if user_input.lower() == 'exit':
-#     print("Exiting the interactive question loop.")
-#     break
-#
-# response = conversational_rag_chain.invoke(
-#     {"input": user_input},
-#     config={"configurable": {"session_id": session_id}}
-# )["answer"]
-#
-# print("\nResponse:")
-# print(response)
-
-
-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
-
-
-def interactive_chat():
-    session_id = "user_session"
-    chat_history = ""
-
-    def chat_loop(question):
-        nonlocal chat_history
-        if question.lower() == "exit":
-            return chat_history + "\nExiting the interactive question loop."
-        response = ask_cancer_question(question, session_id)
-        chat_history += f"\nUser: {question}\nBot: {response}"
-        return chat_history
-
-    return chat_loop
-
-
-demo = gr.Interface(
-                fn=chatbot(),
-                inputs=[gr.Textbox(lines=2, placeholder="Ask a cancer-related question...", elem_id="query_input")],
-                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="Mini GPT Researcher",
-
-# Custom CSS to style the input boxes
-demo.css = """
-    #query_input{
-        border: 2px solid #FFA500;  /* Orange border color */
-        border-radius: 8px;  /* Rounded corners */
-        padding: 10px;  /* Padding inside the input box */
-        margin-bottom: 20px;  /* Space below the input box */
-        transition: border-color 0.3s ease, background-color 0.3s ease;  /* Smooth transition */
-        background-color: #FFEB3B;  /* Orange background color */
-    }
-    #query_input:focus{
-        border-color: #1E90FF;  /* Blue border color on focus */
-        background-color: #87CEFA;  /* Light blue background color on focus */
-    }
-    #query_inputs
-        background-color: #FFEB3B;  /* Orange background color */
-    }
-    #Output {
-        border: 2px solid #1E90FF;  /* Blue border color */
-        border-radius: 8px;  /* Rounded corners */
-        padding: 10px;  /* Padding inside the output box */
-        background-color: #FFEB3B;  /* Light blue background color */
-        margin-top: 20px;  /* Space above the output box */
-        font-size: 16px;  /* Font size for readability */
-    }
-"""
-
-# Launch the app
-if __name__ == '__main__':
-    demo.launch()
-
-
-
+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
+
+# Download the faiss_index folder from Hugging Face
+faiss_index_path = hf_hub_download(
+    repo_id="Dorcatz123/Cancer_Researcher_AI",  # Your Hugging Face Space repo ID
+    filename="faiss_index",  # Folder name in the repo
+    repo_type="space",  # Set this to "space" for a Hugging Face Space
+    use_auth_token=True  # If your repo is private, this ensures authentication
+)
+
+# Load the FAISS index (Adjust path if necessary)
+index_path = os.path.join(faiss_index_path, "faiss_index")  # Path to the downloaded folder
+import faiss
+index = faiss.read_index(index_path)
+
+
+
+try:
+    if api:
+        set_key('.env', 'OPENAI_API_KEY', api)
+except Exception as e:
+    return f"❌ Error: {str(e)}"
+
+load_dotenv()
+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")
+
+# LLM
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
+
+# Initialize OpenAI embeddings
+embeddings = OpenAIEmbeddings()
+
+# 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",
+)
+
+
+# Interactive Loop
+# user_input = input("\nAsk a cancer-related question (or type 'exit' to quit): ")
+#
+# if user_input.lower() == 'exit':
+#     print("Exiting the interactive question loop.")
+#     break
+#
+# response = conversational_rag_chain.invoke(
+#     {"input": user_input},
+#     config={"configurable": {"session_id": session_id}}
+# )["answer"]
+#
+# print("\nResponse:")
+# print(response)
+
+
+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
+
+
+# def interactive_chat():
+#     session_id = "user_session"
+#     chat_history = ""
+
+#     def chat_loop(question):
+#         nonlocal chat_history
+#         if question.lower() == "exit":
+#             return chat_history + "\nExiting the interactive question loop."
+#         response = ask_cancer_question(question, session_id)
+#         chat_history += f"\nUser: {question}\nBot: {response}"
+#         return chat_history
+
+#     return chat_loop
+
+def interactive_chat(question):
+    session_id = "user_session"
+    chat_history = ""
+
+    # Inside chat_loop
+    if question.lower() == "exit":
+        return chat_history + "\nExiting the interactive question loop."
+    
+    response = ask_cancer_question(question, session_id)
+    chat_history += f"\nUser: {question}\nBot: {response}"
+    return chat_history
+
+demo = gr.Interface(
+                fn=chatbot(),
+                inputs=[gr.Textbox(lines=2, placeholder="Ask a cancer-related question...", elem_id="query_input")],
+                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 */
+    }
+
+    #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;
+    }
+"""
+
+
+# Launch the app
+if __name__ == '__main__':
+    demo.launch()
+
+
+