Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,231 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+import requests
+from dotenv import load_dotenv  # Only needed if using a .env file
+import re  # To help clean up leading whitespace
+import sys
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Prefer pysqlite3 (if installed) for SQLite; otherwise fall back to stdlib sqlite3.
+# This mirrors the previous behavior but is robust when pysqlite3 isn't available.
+try:
+    # Try to import the pysqlite3 package which provides a replacement sqlite3 module
+    pysqlite3 = __import__('pysqlite3')
+    # If imported, make it available under the name 'sqlite3' to satisfy code expecting
+    # a sqlite3 module with pysqlite3's behavior.
+    if 'sqlite3' not in sys.modules:
+        sys.modules['sqlite3'] = pysqlite3
+except ModuleNotFoundError:
+    # pysqlite3 isn't installed; the standard library's sqlite3 will be used.
+    import sqlite3  # noqa: F401
+
+# Langchain and HuggingFace
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+# Workaround: some versions of the `groq` package (used by `langchain_groq`) may pass
+# a `proxies` keyword into the underlying Client.__init__, which can cause
+# TypeError if the installed groq Client doesn't accept that kwarg. To make the
+# app more tolerant across versions, monkeypatch the groq Client constructor to
+# silently drop `proxies` if present.
+try:
+    import groq._base_client as _groq_base
+    _groq_client_init = getattr(_groq_base.Client, "__init__", None)
+    if _groq_client_init is not None:
+        def _client_init_no_proxies(self, *args, **kwargs):
+            kwargs.pop('proxies', None)
+            return _groq_client_init(self, *args, **kwargs)
+        _groq_base.Client.__init__ = _client_init_no_proxies
+except Exception:
+    # If groq isn't installed or the internal structure differs, let the import
+    # of ChatGroq attempt to initialize and raise its own errors. We don't
+    # want to crash here just for the monkeypatch.
+    pass
+
+from langchain_groq import ChatGroq
+from langchain.chains import RetrievalQA
+
+# Load the .env file (if using it)
+load_dotenv()
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# Load embeddings, model, and vector store
+@st.cache_resource  # Singleton, prevent multiple initializations
+def init_chain():
+    model_kwargs = {'trust_remote_code': True}
+    embedding = HuggingFaceEmbeddings(model_name='nomic-ai/nomic-embed-text-v1.5', model_kwargs=model_kwargs)
+    llm = ChatGroq(groq_api_key=groq_api_key, model_name="openai/gpt-oss-20b", temperature=0.1)
+    vectordb = Chroma(persist_directory='updated_CSPCDB2', embedding_function=embedding)
+
+    # Create chain
+    chain = RetrievalQA.from_chain_type(llm=llm,
+                                        chain_type="stuff",
+                                        retriever=vectordb.as_retriever(k=5),
+                                        return_source_documents=True)
+    return chain
+
+# Streamlit app layout
+st.set_page_config(
+    page_title="CSPC Citizens Charter Conversational Agent",
+    page_icon="cspclogo.png"
+)
+
+# Custom CSS for styling
+st.markdown(
+    """
+    <style>
+    .subtitle {text-align: center; font-size: 18px; font-weight: bold;}
+    .details {font-size: 14px; color: #bbbbbb; padding-left: 20px; margin-bottom: -10px;}
+    .team {text-align: center; font-size: 20px; font-weight: bold; color: #777;}
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+with st.sidebar:
+    # App title
+    st.title('CSPC Conversational Agent')
+    st.markdown('<p class="subtitle">Your go-to assistant for the Citizen’s Charter of CSPC!</p>', unsafe_allow_html=True)
+    
+    # Categories
+    st.markdown('''✔️**About CSPC:**''')
+    st.markdown('<p class="details">History, Core Values, Mission and Vision</p>', unsafe_allow_html=True)
+    
+    st.markdown('''✔️**Admission & Graduation:**''')
+    st.markdown('<p class="details">Apply, Requirements, Process, Graduation</p>', unsafe_allow_html=True)
+
+    st.markdown('''✔️**Student Services:**''')
+    st.markdown('<p class="details">Scholarships, Orgs, Facilities</p>', unsafe_allow_html=True)
+
+    st.markdown('''✔️**Academics:**''')
+    st.markdown('<p class="details">Degrees, Courses, Faculty</p>', unsafe_allow_html=True)
+
+    st.markdown('''✔️**Officials:**''')
+    st.markdown('<p class="details">President, VPs, Deans, Admin</p>', unsafe_allow_html=True)
+
+    # Links to resources
+    st.markdown("### 🔗 Quick Access to Resources")
+    st.markdown(
+        """
+        📄 [CSPC Citizen’s Charter](https://cspc.edu.ph/governance/citizens-charter/)  
+        🏛️ [About CSPC](https://cspc.edu.ph/about/)  
+        📋 [College Officials](https://cspc.edu.ph/college-officials/)  
+        """,
+        unsafe_allow_html=True
+    )
+
+# Store LLM generated responses
+if "messages" not in st.session_state:
+    st.session_state.chain = init_chain()
+    st.session_state.messages = [{"role": "assistant", "content": "Hello! I am your Conversational Agent for the Citizens Charter of Camarines Sur Polytechnic Colleges (CSPC). How may I assist you today?"}]
+    st.session_state.query_counter = 0  # Track the number of user queries
+    st.session_state.conversation_history = ""  # Keep track of history for the LLM
+
+def generate_response(prompt_input):
+    try:
+        # Retrieve vector database context using ONLY the current user input
+        retriever = st.session_state.chain.retriever
+        relevant_context = retriever.get_relevant_documents(prompt_input)  # Retrieve context only for the current prompt
+        
+        # Format the input for the chain with the retrieved context
+        formatted_input = (
+            f"You are a Conversational Agent for the Citizens Charter of Camarines Sur Polytechnic Colleges (CSPC). "
+            f"Your purpose is to provide accurate and helpful information about CSPC's policies, procedures, and services as outlined in the Citizens Charter. "
+            f"When responding to user queries:\n"
+            f"1. Always prioritize information from the provided context (Citizens Charter or other CSPC resources).\n"
+            f"2. Be concise, clear, and professional in your responses.\n"
+            f"3. If the user's question is outside the scope of the Citizens Charter, politely inform them and suggest relevant resources or departments they can contact.\n\n"
+            f"Context:\n"
+            f"{' '.join([doc.page_content for doc in relevant_context])}\n\n"
+            f"Conversation:\n{st.session_state.conversation_history}user: {prompt_input}\n"
+        )
+    
+        # Invoke the RetrievalQA chain directly with the formatted input
+        res = st.session_state.chain.invoke({"query": formatted_input})
+        
+        # Process the response text
+        result_text = res['result']
+        
+        # Clean up prefixing phrases and capitalize the first letter
+        if result_text.startswith('According to the provided context, '):
+            result_text = result_text[35:].strip()
+        elif result_text.startswith('Based on the provided context, '):
+            result_text = result_text[31:].strip()
+        elif result_text.startswith('According to the provided text, '):
+            result_text = result_text[34:].strip()
+        elif result_text.startswith('According to the context, '):
+            result_text = result_text[26:].strip()
+        
+        # Ensure the first letter is uppercase
+        result_text = result_text[0].upper() + result_text[1:] if result_text else result_text
+    
+        # Extract and format sources (if available)
+        sources = []
+        for doc in relevant_context:
+            source_path = doc.metadata.get('source', '')
+            formatted_source = source_path[122:-4] if source_path else "Unknown source"
+            sources.append(formatted_source)
+        
+        # Remove duplicates and combine into a single string
+        unique_sources = list(set(sources))
+        source_list = ", ".join(unique_sources)
+        
+        # # Combine response text with sources
+        # result_text += f"\n\n**Sources:** {source_list}" if source_list else "\n\n**Sources:** None"
+        
+        # Update conversation history
+        st.session_state.conversation_history += f"user: {prompt_input}\nassistant: {result_text}\n"
+    
+        return result_text
+
+    except Exception as e:
+        # Handle rate limit or other errors gracefully
+        if "rate_limit_exceeded" in str(e).lower():
+            return "⚠️ Rate limit exceeded. Please clear the chat history and try again."
+        else:
+            return f"❌ An error occurred: {str(e)}"
+
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# User-provided prompt for input box
+if prompt := st.chat_input(placeholder="Ask a question..."):
+    # Increment query counter
+    st.session_state.query_counter += 1
+    # Append user query to session state
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+    # Generate and display placeholder for assistant response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()  # Placeholder for response while it's being generated
+        with st.spinner("Generating response..."):
+            # Use conversation history when generating response
+            response = generate_response(prompt)
+        message_placeholder.markdown(response)  # Replace placeholder with actual response
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+    # Check if query counter has reached the limit
+    if st.session_state.query_counter >= 10:
+        st.sidebar.warning("Conversation context has been reset after 10 queries.")
+        st.session_state.query_counter = 0  # Reset the counter
+        st.session_state.conversation_history = ""  # Clear conversation history for the LLM
+
+# Clear chat history function
+def clear_chat_history():
+    # Clear chat messages (reset the assistant greeting)
+    st.session_state.messages = [{"role": "assistant", "content": "Hello! I am your Conversational Agent for the Citizens Charter of Camarines Sur Polytechnic Colleges (CSPC). How may I assist you today?"}]
+    
+    # Reinitialize the chain to clear any stored history (ensures it forgets previous user inputs)
+    st.session_state.chain = init_chain()
+
+    # Clear the query counter and conversation history
+    st.session_state.query_counter = 0
+    st.session_state.conversation_history = ""
+
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+
+# Footer
+st.sidebar.markdown('<p class="team">Developed by Team XceptionNet</p>', unsafe_allow_html=True)