Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import os
+import tempfile
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+import requests
+from bs4 import BeautifulSoup
+from langchain.agents import initialize_agent, Tool, AgentType
+from operator import itemgetter
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="Chatbot Excel",
+    page_icon="📊",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+# --- Custom Styles ---
+st.markdown(
+    """
+    <style>
+    .stButton > button {
+        background-color: #007bff;
+        color: white;
+        border: none;
+        border-radius: 5px;
+        padding: 0.5em 1em;
+        font-size: 1em;
+        font-weight: 600;
+    }
+    .greeting-text {
+        font-size: 2.5em;
+        color: transparent;
+        background-image: linear-gradient(90deg, #00529B, #00A9E0);
+        -webkit-background-clip: text;
+        font-weight: 600;
+        text-align: center !important;
+    }
+    .sidebar .sidebar-content {
+        background-color: #f0f2f6;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# --- Helper function to format chat history ---
+def format_chat_history(chat_messages_list):
+    """Formats chat history for LLM prompt, excluding the last user message."""
+    history_for_prompt = chat_messages_list[:-1]
+    
+    if not history_for_prompt:
+        return "No conversation history available."
+    
+    formatted_history = []
+    for msg in history_for_prompt:
+        role_label = "User" if msg["role"] == "user" else "Assistant"
+        formatted_history.append(f"{role_label}: {msg['content']}")
+    return "\n".join(formatted_history)
+
+# --- Bing Web Search Function ---
+def bing_search_tool_function(query: str) -> str:
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    }
+    quoted_query = requests.utils.quote(query)
+    search_url = f"https://www.bing.com/search?q={quoted_query}&qs=HS&pq=se&sc=10-2&cvid=C9D3906F723C49862C937B28F8106C8C&FORM=QBLH&sp=1&lq=0"
+    results_list = []
+    try:
+        response = requests.get(search_url, headers=headers, timeout=10)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, "html.parser")
+        for item in soup.find_all("li", attrs={"class": "b_algo"}):
+            title_tag = item.find("h2")
+            title = title_tag.get_text().strip() if title_tag else "No title"
+            link_tag = title_tag.find("a") if title_tag else None
+            link = link_tag["href"] if link_tag else "No link"
+            description_text = "No description available."
+            description_tag = item.find(class_="b_caption")
+            if description_tag:
+                description_text = description_tag.get_text().strip()
+            elif item.find("p"):
+                caption_div = item.find("p")
+                if caption_div:
+                    description_text = caption_div.get_text().strip()
+            description_text = description_text + " url:" + link
+            if title != "No title" or description_text != "No description available.":
+                 results_list.append({"title": title, "description": description_text})
+        if not results_list:
+            return "Unfortunately, I couldn't find any results on the web for this search."
+        formatted_output = "\n\n".join([
+            f"Title: {res['title']}\nDescription: {res['description']}" 
+            for res in results_list[:5]
+        ])
+        return formatted_output
+    except requests.exceptions.RequestException as e:
+        return f"Network error in web search: {e}"
+    except Exception as e:
+        return f"Error parsing search results: {e}"
+
+# --- Main function for Q&A from data and web search ---
+def chat_with_data_and_web(api_key, base_url):
+    st.write('<div class="greeting-text">Hello! Welcome to Chatbot Excel.</div>', unsafe_allow_html=True)
+
+    # --- Function to completely reset chat and RAG state ---
+    def reset_all_chat_and_rag_state():
+        keys_to_clear = ['rag_initialized_for_file', 'retriever', 'chat_messages']
+        for key in keys_to_clear:
+            if key in st.session_state:
+                del st.session_state[key]
+        
+        st.session_state.chat_messages = [{"role": "assistant", "content": "Hello! Upload a file to get started or ask me to search the web."}]
+        
+        # Reset file_uploader by changing its key
+        if 'uploader_key_suffix_counter' not in st.session_state:
+            st.session_state.uploader_key_suffix_counter = 0
+        st.session_state.uploader_key_suffix_counter += 1
+        
+        st.rerun()
+
+    # --- Function to reset RAG state on file change (without clearing chat history) ---
+    def reset_rag_on_file_change():
+        keys_to_clear = ['rag_initialized_for_file', 'retriever']
+        for key in keys_to_clear:
+            if key in st.session_state:
+                del st.session_state[key]
+        
+        if "chat_messages" not in st.session_state:
+             st.session_state.chat_messages = []
+        st.session_state.chat_messages.append({
+            "role": "assistant",
+            "content": "New file detected. Preparing for Q&A..."
+        })
+
+    # --- "Start New Chat" button in sidebar ---
+    if st.sidebar.button("Start New Chat", key="new_chat_btn"):
+        reset_all_chat_and_rag_state()
+
+    # Initialize uploader key counter if not exists
+    if 'uploader_key_suffix_counter' not in st.session_state:
+        st.session_state.uploader_key_suffix_counter = 0
+    
+    current_uploader_key = f"main_file_uploader_{st.session_state.uploader_key_suffix_counter}"
+
+    uploaded_file = st.file_uploader(
+        "Upload Excel or CSV file (optional for web search):",
+        type=["xlsx", "csv"],
+        key=current_uploader_key,
+        on_change=reset_rag_on_file_change
+    )
+
+    if "chat_messages" not in st.session_state:
+        st.session_state.chat_messages = [{"role": "assistant", "content": "Upload a file to ask questions about its content, or ask me to search the web."}]
+
+    for msg in st.session_state.chat_messages:
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["content"])
+
+    # --- File processing logic ---
+    if uploaded_file is not None and st.session_state.get('rag_initialized_for_file') != uploaded_file.name:
+        with st.spinner(f"Processing file {uploaded_file.name} for Q&A..."):
+            tmp_file_path = None
+            try:
+                with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp_file:
+                    tmp_file.write(uploaded_file.getvalue())
+                    tmp_file_path = tmp_file.name
+
+                if uploaded_file.name.endswith(".xlsx"):
+                    df = pd.read_excel(tmp_file_path)
+                elif uploaded_file.name.endswith(".csv"):
+                    df = pd.read_csv(tmp_file_path, encoding='utf-8')
+                else:
+                    st.error("File type not supported.")
+                    if 'rag_initialized_for_file' in st.session_state: del st.session_state['rag_initialized_for_file']
+                    if 'retriever' in st.session_state: del st.session_state.retriever
+                    return
+
+                st.write("### Data Preview (first 5 rows):")
+                st.dataframe(df.head())
+
+                if df.empty:
+                    st.warning("The uploaded file is empty. Q&A from file will not be effective.")
+                    st.session_state.rag_initialized_for_file = "empty_file_" + uploaded_file.name
+                    st.session_state.chat_messages.append({"role": "assistant", "content": f"File '{uploaded_file.name}' is empty. Would you like me to search the web?"})
+                    st.rerun()
+                    return
+
+                documents_for_rag = [f"Row {idx}: " + ", ".join([f"{col}: {str(val)}" for col, val in row.items() if pd.notna(val)]) for idx, row in df.iterrows()]
+                
+                if not documents_for_rag:
+                    st.warning("No data extracted from file for Q&A.")
+                    st.session_state.rag_initialized_for_file = "no_docs_" + uploaded_file.name
+                    st.session_state.chat_messages.append({"role": "assistant", "content": f"No data extracted from file '{uploaded_file.name}' for processing."})
+                    st.rerun()
+                    return
+                
+                text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+                all_splits = text_splitter.create_documents(documents_for_rag)
+
+                if not all_splits:
+                    st.warning("Text splitting resulted in no chunks. File may be too small for Q&A.")
+                    st.session_state.rag_initialized_for_file = "no_splits_" + uploaded_file.name
+                    st.session_state.chat_messages.append({"role": "assistant", "content": f"File '{uploaded_file.name}' was too small for splitting and processing."})
+                    st.rerun()
+                    return
+                
+                embeddings_model = OpenAIEmbeddings(
+                    openai_api_key=api_key,
+                    model="text-embedding-3-small",
+                    base_url=base_url
+                )
+                vectorstore = FAISS.from_documents(all_splits, embeddings_model)
+                st.session_state.retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+                st.session_state.rag_initialized_for_file = uploaded_file.name
+                
+                st.session_state.chat_messages.append({
+                    "role": "assistant",
+                    "content": f"File '{uploaded_file.name}' successfully processed. You can now ask questions about its content or request web searches."
+                })
+                st.rerun()
+
+            except Exception as e:
+                st.error(f"Error processing file for Q&A: {e}")
+                if 'rag_initialized_for_file' in st.session_state: del st.session_state['rag_initialized_for_file']
+                if 'retriever' in st.session_state: del st.session_state.retriever
+                st.session_state.chat_messages.append({"role": "assistant", "content": f"Error processing file: {e}"})
+                st.rerun()
+            finally:
+                if tmp_file_path and os.path.exists(tmp_file_path):
+                    os.remove(tmp_file_path)
+    
+    if prompt := st.chat_input("Ask your question or tell me to search the web:"):
+        st.session_state.chat_messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            message_placeholder.markdown("Thinking...")
+
+            search_keywords = ["search for", "look up", "find", "search", "web search"]
+            is_search_request = any(prompt.lower().startswith(kw) for kw in search_keywords)
+            
+            search_query = prompt
+            for kw in search_keywords:
+                if prompt.lower().startswith(kw):
+                    search_query = prompt[len(kw):].strip()
+                    break
+
+            response_text = ""
+            try:
+                if not api_key:
+                    error_msg = "OpenAI API key not provided. Please enter it in the sidebar."
+                    message_placeholder.error(error_msg)
+                    st.session_state.chat_messages.append({"role": "assistant", "content": error_msg})
+                    return
+
+                llm_rag = ChatOpenAI(
+                    model="gpt-4o-mini",
+                    temperature=0.2,
+                    openai_api_key=api_key,
+                    base_url=base_url
+                )
+
+                if is_search_request and search_query:
+                    message_placeholder.markdown(f"Searching the web for: '{search_query}' using agent...")
+                    llm_for_agent = ChatOpenAI(
+                        model="gpt-4o-mini",
+                        temperature=0.7,
+                        openai_api_key=api_key,
+                        base_url=base_url
+                    )
+                    tools = [
+                        Tool(
+                            name="BingSearch",
+                            func=bing_search_tool_function,
+                            description="Search the web using Bing and provide detailed results"
+                        )
+                    ]
+                    agent = initialize_agent(
+                        tools,
+                        llm_for_agent,
+                        agent_type=AgentType.REACT_DOCSTORE,
+                        verbose=True,
+                        handle_parsing_errors=True
+                    )
+                    try:
+                        response_text = agent.run(search_query)
+                    except Exception as agent_exc:
+                        st.error(f"Error running search agent: {agent_exc}")
+                        response_text = f"Unfortunately, there was a problem processing the web search: {agent_exc}"
+                
+                elif uploaded_file and 'retriever' in st.session_state and st.session_state.get('rag_initialized_for_file') == uploaded_file.name:
+                    retriever = st.session_state.retriever
+                    
+                    rag_prompt_template = """Based on the previous conversation history and the text below extracted from the uploaded file, answer the user's question.
+                    If the information is not available in the text, state that the information was not found in the provided data. Do not make up an answer.
+
+                    Conversation History:
+                    {chat_history}
+
+                    Extracted Text:
+                    {context}
+
+                    Current User Question: {question}
+
+                    Answer:"""
+                    rag_prompt = PromptTemplate.from_template(rag_prompt_template)
+
+                    def format_docs(docs):
+                        return "\n\n".join(doc.page_content for doc in docs)
+
+                    rag_chain = (
+                        {
+                            "context": itemgetter("question") | retriever | format_docs,
+                            "question": itemgetter("question"),
+                            "chat_history": itemgetter("chat_history")
+                        }
+                        | rag_prompt
+                        | llm_rag
+                        | StrOutputParser()
+                    )
+                    
+                    formatted_history = format_chat_history(st.session_state.chat_messages)
+                    
+                    response_text = rag_chain.invoke({
+                        "question": prompt,
+                        "chat_history": formatted_history
+                    })
+
+                elif uploaded_file and st.session_state.get('rag_initialized_for_file') != uploaded_file.name:
+                    response_text = f"File '{uploaded_file.name}' is still being processed or encountered an issue. Please wait or re-upload. You can also ask me to search the web."
+                elif not uploaded_file and not is_search_request:
+                     response_text = "Please upload a file to ask questions about its content, or ask me to search the web (e.g., 'search for...')."
+                elif not search_query and is_search_request:
+                    response_text = "It seems you wanted to search, but didn't specify what to search for. Please try again, e.g., 'search for latest tech news'."
+
+                message_placeholder.markdown(response_text)
+                st.session_state.chat_messages.append({"role": "assistant", "content": response_text})
+
+            except Exception as e:
+                error_msg = f"An error occurred: {e}"
+                st.error(error_msg)
+                if not response_text:
+                    message_placeholder.markdown(f"Unfortunately, something went wrong: {e}")
+                st.session_state.chat_messages.append({"role": "assistant", "content": f"Unfortunately, something went wrong: {e}"})
+                
+    elif not uploaded_file and len(st.session_state.get("chat_messages", [])) <= 1:
+         st.info("Upload an Excel or CSV file to chat about its data, or tell me to search the web.")
+
+# --- Main UI Layout ---
+def main():
+    st.sidebar.info("Ask questions about uploaded data or request web searches.")
+    st.title("Chatbot Excel")
+    
+    api_key = st.sidebar.text_input("Enter your OpenAI API Key:", type="password", key="main_api_key_input")
+    base_url = st.sidebar.text_input("Enter API Base URL:", key="base_url_input")
+
+    if api_key and base_url:
+        chat_with_data_and_web(api_key, base_url)
+    else:
+        st.sidebar.warning("Please enter your OpenAI API key and base URL to continue.")
+        st.info("Please enter your OpenAI API key and base URL in the sidebar to use the application features.")
+
+if __name__ == "__main__":
+    main()