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streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import os
+from langchain_community.document_loaders import DataFrameLoader
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+
+# --- 1. Page Configuration ---
+st.set_page_config(
+    page_title="Quranic Insight AI",
+    page_icon="🕋",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# --- 2. Custom CSS for Theming and Design ---
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Merriweather:wght@300;400;700&family=Amiri&display=swap');
+
+    /* Main background with geometric pattern */
+    .stApp {
+        background-color: #1a1a1a; /* Dark Charcoal */
+        background-image: linear-gradient(315deg, rgba(255, 255, 255, 0.02) 25%, transparent 25%),
+                          linear-gradient(45deg, rgba(255, 255, 255, 0.02) 25%, transparent 25%);
+        background-size: 20px 20px;
+        color: #e0e0e0; /* Off-white text */
+    }
+
+    /* Main title font and color */
+    h1 {
+        font-family: 'Amiri', serif;
+        color: #d4af37; /* Soft Gold */
+        text-align: center;
+        padding-top: 2rem;
+    }
+
+    /* Subtitle style */
+    .subtitle {
+        font-family: 'Merriweather', serif;
+        color: #b0b0b0;
+        text-align: center;
+        font-size: 1.1rem;
+    }
+    
+    /* Sidebar styling */
+    .st-emotion-cache-16txtl3 {
+        background-color: #212121;
+    }
+
+    /* Chat message styling */
+    .st-emotion-cache-1c7y2kd { /* Chat message container */
+        background-color: rgba(42, 42, 42, 0.8);
+        border: 1px solid #d4af37;
+        border-radius: 12px;
+        margin-bottom: 1rem;
+    }
+    
+    /* Input box styling */
+    .st-emotion-cache-1jicfl2 {
+        background-color: #2a2a2a;
+    }
+
+    /* Output formatting improvements */
+    .stMarkdown h3 {
+        color: #50c878; /* Mint Green for headings */
+        border-bottom: 2px solid #d4af37;
+        padding-bottom: 5px;
+    }
+    .stMarkdown blockquote {
+        background-color: rgba(212, 175, 55, 0.1);
+        border-left: 5px solid #d4af37;
+        padding: 0.5rem 1rem;
+        margin-left: 0;
+        border-radius: 5px;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+
+# --- 3. Cached Functions for Heavy Lifting ---
+@st.cache_resource
+def load_rag_chain():
+    load_dotenv()
+    
+    llm = ChatOpenAI(model="gpt-4o", temperature=0.1)
+    embeddings = SentenceTransformerEmbeddings(model_name="paraphrase-multilingual-mpnet-base-v2")
+
+    csv_filename = 'quran_multilingual_data.csv'
+    if not os.path.exists(csv_filename):
+        st.error(f"CRITICAL ERROR: The data file '{csv_filename}' was not found.")
+        st.stop()
+    
+    df = pd.read_csv(csv_filename)
+    df.fillna("", inplace=True)
+
+    df['page_content'] = "Reference: " + df['reference'].astype(str) + "\n" + \
+                         "Urdu Translation 1: " + df['translation_maududi'] + "\n" + \
+                         "Urdu Translation 2: " + df['translation_qadri'] + "\n" + \
+                         "English Translation: " + df['translation_english']
+    
+    loader = DataFrameLoader(df, page_content_column='page_content')
+    documents = loader.load()
+
+    persist_directory = "./quran_multilingual_db"
+    if os.path.exists(persist_directory):
+        vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    else:
+        with st.spinner("Creating new multilingual database. This might take a few minutes..."):
+            vectorstore = Chroma.from_documents(documents, embeddings, persist_directory=persist_directory)
+
+    retriever = vectorstore.as_retriever(search_kwargs={'k': 7})
+    
+    # --- New and Improved Prompt Template for Better Formatting ---
+    prompt_template = """
+    You are an expert and respectful Quranic Assistant. Your task is to follow a strict, step-by-step process to answer the user's question based ONLY on the context, using precise Markdown formatting.
+
+    **Your Thought Process (Follow these steps internally):**
+1.  **Step 1: Identify Language.** Analyze the user's `Question` to determine if it is in English or Roman Urdu. This decision is critical and will control the language of your entire response.
+2.  **Step 2: Synthesize a Summary.** Based on the language identified in Step 1, carefully read the user's question and understand it and then read the `Context` and formulate a 3-4 line summary that directly answers the `Question`.
+3.  **Step 3: Format Detailed Points.** Create a numbered list of key points from the `Context`. For each point, you must follow these sub-rules precisely:
+    -   **Sub-rule 3a:** If the identified language was English, you MUST use the "English Translation" from the context for the `Translation:` field.
+    -   **Sub-rule 3b:** If the identified language was Roman Urdu, you MUST use one of the "Urdu Translation" texts from the context for the `Translation:` field.
+    -   **Sub-rule 3c:** The `Explanation:` must be in the same language as the `Question`.
+
+    ---
+
+    ### Detailed Points
+    (Create a numbered list of key points below.)
+
+    1.  **Translation:**
+        > (The appropriate translation text goes here, inside a blockquote.)
+        **Reference:** `[The verse reference, e.g., 2:153]`
+        
+        **Explanation:** (Your 1-2 line explanation for this point goes here.)
+
+    2.  **Translation:**
+        > (The second translation text goes here.)
+        **Reference:** `[The second verse reference]`
+        
+        **Explanation:** (The explanation for the second point.)
+    
+    (and so on...Try to give as much points as you can generate)
+
+    **Context from Database:**
+    {context}
+
+    **User's Question:**
+    {question}
+
+    **Your Final Answer (Strictly follow the Markdown format above):**
+    """
+    prompt = ChatPromptTemplate.from_template(prompt_template)
+
+    rag_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    return rag_chain
+
+# --- 4. Main App Interface ---
+
+# Load the RAG chain (fast due to caching)
+rag_chain = load_rag_chain()
+
+# Sidebar for information
+with st.sidebar:
+    st.title("About Quranic Insight AI")
+    st.markdown("""
+    This is an AI-powered assistant designed to help you explore the teachings of the Holy Quran. 
+    
+    **How it works:**
+    1.  Ask a question in English or Roman Urdu.
+    2.  The AI searches through multiple translations of the Quran to find the most relevant verses.
+    3.  It then uses a powerful language model to generate a structured and informative answer based on those verses.
+    
+    **Data Sources:**
+    - Arabic Text: Tanzil.net
+    - Urdu Translations: Maududi & Tahir-ul-Qadri
+    - English Translation: Abdullah Yusuf Ali
+    """)
+    st.info("This is an experimental AI project. Always consult with a qualified Islamic scholar for definitive religious guidance.")
+
+# Main page title
+st.title("Quranic Insight AI | قرآنی معاون")
+st.markdown("<p class='subtitle'>Your AI assistant for exploring the Quran</p>", unsafe_allow_html=True)
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": "As-salamu alaykum! How can I help you explore the Quran today?"}]
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("Ask a question about the Quran..."):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        with st.spinner("Analyzing verses..."):
+            response = rag_chain.invoke(prompt)
+            st.markdown(response, unsafe_allow_html=True)
+    
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})