"Donate" button added

app.py

@@ -1,148 +1,158 @@
-import streamlit as st
-import os
-from langchain.embeddings import SentenceTransformerEmbeddings
-from langchain.vectorstores import FAISS
-from langchain_openai import ChatOpenAI
-from langchain.chains import RetrievalQA
-from langchain.prompts import ChatPromptTemplate
-
-# --- Path Configurations ---
-VECTOR_DB_PATH = "vector_db"
-EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2"
-
-# --- 1. Load Resources (Vector Database and Embedding Model) ---
-# @st.cache_resource loads these components only once when the Streamlit app starts
-@st.cache_resource
-def load_resources():
-    st.spinner("Loading embedding model...")
-    print(f"Loading embedding model: {EMBEDDING_MODEL_NAME}...")
-    embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL_NAME)
-    print("Embedding model loaded.")
-
-    st.spinner("Loading vector database...")
-    print(f"Loading FAISS vector database from: {VECTOR_DB_PATH}...")
-    # allow_dangerous_deserialization=True is needed for FAISS.load_local
-    # It's safe to use if you generated the database yourself.
-    vector_db = FAISS.load_local(VECTOR_DB_PATH, embeddings, allow_dangerous_deserialization=True)
-    print("Vector database loaded.")
-
-    return embeddings, vector_db
-
-embeddings, vector_db = load_resources()
-
-# --- 2. Load and Configure the OpenAI LLM (GPT-4o) ---
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-if openai_api_key:
-    try:
-        llm = ChatOpenAI(
-            temperature=0.85, # Controls creativity/randomness (0.0 to 1.0)
-            api_key=openai_api_key,
-            model_name="gpt-4o",
-            model_kwargs={"top_p": 0.9} # Controls diversity of output
-        )
-        st.success("OpenAI model (gpt-4o) loaded successfully!")
-    except Exception as e:
-        st.error(f"Error initializing OpenAI model. Check your API key, "
-                 f"model name, and plan/quotas: {e}")
-        st.stop() # Stop the app if LLM cannot be initialized
-else:
-    st.error("OpenAI API Key (OPENAI_API_KEY) not found in environment variables.")
-    st.stop() # Stop the app if API key is not found
-
-# --- 3. Define the System Prompt for Assistant Behavior ---
-SYSTEM_PROMPT_TEMPLATE = """
-You are a friendly, experienced, and patient study tutor specializing in Databricks.
-Your goal is to help the user deeply understand topics from Databricks documentation to prepare for Databricks certifications.
-
-Follow these guidelines:
-1.  **Always respond in the same language as the user's question.** If the question is in Portuguese, reply in Portuguese. If it's in English, reply in English.
-2.  **Explain clearly and concisely:** Use accessible language and avoid unnecessary jargon where possible.
-3.  **Go beyond simple retrieval:** Do not just reproduce information. Interpret it, reorganize it, and present it in a didactic way.
-4.  **Provide practical examples:** If appropriate, create small examples or analogies to illustrate the concept within the context of Databricks or data engineering scenarios.
-5.  **Maintain an encouraging and motivating tone:** Encourage the user in their learning.
-6.  **Use the provided "Context Documents" to answer the question.** Prioritize information from these documents.
-7.  **If the answer is not in the context documents, be honest:** State that you could not find the information and suggest the user search other sources or rephrase the question. Do not invent information.
-8.  Format your responses legibly, using lists, bold text, or code blocks when appropriate.
-
-Context Documents:
-{context}
-
-User Question:
-{question}
-"""
-
-# Create a ChatPromptTemplate from the System Prompt
-qa_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", SYSTEM_PROMPT_TEMPLATE),
-        ("human", "{question}") # Where the user's question will be inserted
-    ]
-)
-
-# --- 4. Configure the RAG Chain (RetrievalQA) ---
-print("Configuring the RAG chain...")
-qa_chain = RetrievalQA.from_chain_type(
-    llm=llm, # <-- THIS IS THE CORRECTED LINE!
-    chain_type="stuff", # 'stuff' strategy puts all retrieved documents directly into the LLM's prompt
-    retriever=vector_db.as_retriever(search_kwargs={"k": 4}), # Configure FAISS as the retriever
-                                                              # k=4 means it retrieves the 4 most relevant chunks
-    return_source_documents=True, # Optional: returns the documents that were used for the answer
-    chain_type_kwargs={"prompt": qa_prompt} # Pass the custom prompt to the chain
-)
-print("RAG chain configured.")
-
-# --- 5. Streamlit Interface ---
-st.set_page_config(
-    page_title="📚 Databricks Study Assistant with RAG",
-    layout="wide",
-    initial_sidebar_state="collapsed"
-)
-
-st.title("📚 Databricks Study Assistant with RAG") # Updated title text
-
-# Updated objective description
-st.markdown("""
-    This assistant is designed to provide you with precise, context-aware answers directly sourced from the official Azure Databricks documentation.
-    It aims to significantly aid your studies for Databricks certifications and streamline the process of resolving technical challenges by offering a more fluid and natural consultation experience.
-""")
-
-# Updated context description
-st.markdown("""
-    This assistant's knowledge base is built upon the official Azure Databricks documentation
-    ([https://learn.microsoft.com/en-us/azure/databricks/](https://learn.microsoft.com/en-us/azure/databricks/))
-    and the official Databricks Azure Knowledge Base
-    ([https://kb.databricks.com/](https://kb.databricks.com/)).
-""")
-
-user_query = st.text_input(
-    "Your question about Databricks documentation:",
-    placeholder="Ex: How to configure Auto Loader in Databricks?"
-)
-
-if st.button("Get Answer", type="primary"):
-    if user_query:
-        with st.spinner("Searching and generating response..."):
-            try:
-                response = qa_chain({"query": user_query})
-                st.subheader("Answer:")
-                st.markdown(response["result"]) # Use markdown for formatting the response
-
-                st.subheader("Source Documents:")
-                if response["source_documents"]:
-                    for i, doc in enumerate(response["source_documents"]):
-                        st.write(f"**Page/Source {i+1}:**")
-                        st.info(doc.page_content) # Content of the chunk
-                        if 'page' in doc.metadata: # If the PDF loader added the page number
-                            st.write(f"*(Page: {doc.metadata['page'] + 1})*") # +1 because it's 0-based
-                        st.markdown("---")
-                else:
-                    st.info("No relevant source documents found for this question.")
-            except Exception as e:
-                st.error(f"An error occurred while processing your question: {e}")
-                st.info("Please check your OpenAI API key, model name, and plan/quotas.")
-    else:
-        st.warning("Please type your question before submitting.")
-
-st.markdown("---")
+import streamlit as st
+import os
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import FAISS
+from langchain_openai import ChatOpenAI
+from langchain.chains import RetrievalQA
+from langchain.prompts import ChatPromptTemplate
+
+# --- Path Configurations ---
+VECTOR_DB_PATH = "vector_db"
+EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2"
+
+# --- 1. Load Resources (Vector Database and Embedding Model) ---
+# @st.cache_resource loads these components only once when the Streamlit app starts
+@st.cache_resource
+def load_resources():
+    st.spinner("Loading embedding model...")
+    print(f"Loading embedding model: {EMBEDDING_MODEL_NAME}...")
+    embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+    print("Embedding model loaded.")
+
+    st.spinner("Loading vector database...")
+    print(f"Loading FAISS vector database from: {VECTOR_DB_PATH}...")
+    # allow_dangerous_deserialization=True is needed for FAISS.load_local
+    # It's safe to use if you generated the database yourself.
+    vector_db = FAISS.load_local(VECTOR_DB_PATH, embeddings, allow_dangerous_deserialization=True)
+    print("Vector database loaded.")
+
+    return embeddings, vector_db
+
+embeddings, vector_db = load_resources()
+
+# --- 2. Load and Configure the OpenAI LLM (GPT-4o) ---
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+if openai_api_key:
+    try:
+        llm = ChatOpenAI(
+            temperature=0.85, # Controls creativity/randomness (0.0 to 1.0)
+            api_key=openai_api_key,
+            model_name="gpt-4o",
+            model_kwargs={"top_p": 0.9} # Controls diversity of output
+        )
+        st.success("OpenAI model (gpt-4o) loaded successfully!")
+    except Exception as e:
+        st.error(f"Error initializing OpenAI model. Check your API key, "
+                 f"model name, and plan/quotas: {e}")
+        st.stop() # Stop the app if LLM cannot be initialized
+else:
+    st.error("OpenAI API Key (OPENAI_API_KEY) not found in environment variables.")
+    st.stop() # Stop the app if API key is not found
+
+# --- 3. Define the System Prompt for Assistant Behavior ---
+SYSTEM_PROMPT_TEMPLATE = """
+You are a friendly, experienced, and patient study tutor specializing in Databricks.
+Your goal is to help the user deeply understand topics from Databricks documentation to prepare for Databricks certifications.
+
+Follow these guidelines:
+1.  **Always respond in the same language as the user's question.** If the question is in Portuguese, reply in Portuguese. If it's in English, reply in English.
+2.  **Explain clearly and concisely:** Use accessible language and avoid unnecessary jargon where possible.
+3.  **Go beyond simple retrieval:** Do not just reproduce information. Interpret it, reorganize it, and present it in a didactic way.
+4.  **Provide practical examples:** If appropriate, create small examples or analogies to illustrate the concept within the context of Databricks or data engineering scenarios.
+5.  **Maintain an encouraging and motivating tone:** Encourage the user in their learning.
+6.  **Use the provided "Context Documents" to answer the question.** Prioritize information from these documents.
+7.  **If the answer is not in the context documents, be honest:** State that you could not find the information and suggest the user search other sources or rephrase the question. Do not invent information.
+8.  Format your responses legibly, using lists, bold text, or code blocks when appropriate.
+
+Context Documents:
+{context}
+
+User Question:
+{question}
+"""
+
+# Create a ChatPromptTemplate from the System Prompt
+qa_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", SYSTEM_PROMPT_TEMPLATE),
+        ("human", "{question}") # Where the user's question will be inserted
+    ]
+)
+
+# --- 4. Configure the RAG Chain (RetrievalQA) ---
+print("Configuring the RAG chain...")
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm, # <-- THIS IS THE CORRECTED LINE!
+    chain_type="stuff", # 'stuff' strategy puts all retrieved documents directly into the LLM's prompt
+    retriever=vector_db.as_retriever(search_kwargs={"k": 4}), # Configure FAISS as the retriever
+                                                              # k=4 means it retrieves the 4 most relevant chunks
+    return_source_documents=True, # Optional: returns the documents that were used for the answer
+    chain_type_kwargs={"prompt": qa_prompt} # Pass the custom prompt to the chain
+)
+print("RAG chain configured.")
+
+# --- 5. Streamlit Interface ---
+st.set_page_config(
+    page_title="📚 Databricks Study Assistant with RAG",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+# Create columns layout
+col1, col2 = st.columns([3, 1])
+
+with col1:
+    st.title("📚 Databricks Study Assistant with RAG") # Updated title text
+
+    # Updated objective description
+    st.markdown("""
+        This assistant is designed to provide you with precise, context-aware answers directly sourced from the official Azure Databricks documentation.
+        It aims to significantly aid your studies for Databricks certifications and streamline the process of resolving technical challenges by offering a more fluid and natural consultation experience.
+    """)
+
+    # Updated context description
+    st.markdown("""
+        This assistant's knowledge base is built upon the official Azure Databricks documentation
+        ([https://learn.microsoft.com/en-us/azure/databricks/](https://learn.microsoft.com/en-us/azure/databricks/))
+        and the official Databricks Azure Knowledge Base
+        ([https://kb.databricks.com/](https://kb.databricks.com/)).
+    """)
+
+    user_query = st.text_input(
+        "Your question about Databricks documentation:",
+        placeholder="Ex: How to configure Auto Loader in Databricks?"
+    )
+
+    if st.button("Get Answer", type="primary"):
+        if user_query:
+            with st.spinner("Searching and generating response..."):
+                try:
+                    response = qa_chain({"query": user_query})
+                    st.subheader("Answer:")
+                    st.markdown(response["result"]) # Use markdown for formatting the response
+
+                    st.subheader("Source Documents:")
+                    if response["source_documents"]:
+                        for i, doc in enumerate(response["source_documents"]):
+                            st.write(f"**Page/Source {i+1}:**")
+                            st.info(doc.page_content) # Content of the chunk
+                            if 'page' in doc.metadata: # If the PDF loader added the page number
+                                st.write(f"*(Page: {doc.metadata['page'] + 1})*") # +1 because it's 0-based
+                            st.markdown("---")
+                    else:
+                        st.info("No relevant source documents found for this question.")
+                except Exception as e:
+                    st.error(f"An error occurred while processing your question: {e}")
+                    st.info("Please check your OpenAI API key, model name, and plan/quotas.")
+        else:
+            st.warning("Please type your question before submitting.")
+
+with col2:
+    st.markdown("<br><br>", unsafe_allow_html=True)  # Espaço para baixo
+    st.markdown('<div style="text-align: right;">', unsafe_allow_html=True)
+    st.image("donate.png", width=180)
+    st.markdown('</div>', unsafe_allow_html=True)
+
+st.markdown("---")
 st.caption("Developed by you, with LangChain, Streamlit, and LLMs.")