Update final.py

final.py

@@ -1,130 +1,131 @@
-import os
-import streamlit as st
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
-from langchain_community.vectorstores import FAISS
-from langchain_core.prompts import PromptTemplate
-from langchain_huggingface import HuggingFaceEndpoint
-from dotenv import load_dotenv, find_dotenv
-
-# ✅ Load environment variables
-load_dotenv(find_dotenv())
-
-# ✅ FAISS Database Path
-DB_FAISS_PATH = "vectorstore/db_faiss"
-
-@st.cache_resource
-def get_vectorstore():
-    """Loads the FAISS vector store with embeddings."""
-    try:
-        embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-        return FAISS.load_local(DB_FAISS_PATH, embedding_model, allow_dangerous_deserialization=True)
-    except Exception as e:
-        st.error(f"⚠️ Error loading vector store: {str(e)}")
-        return None
-
-@st.cache_resource
-def load_llm():
-    """Loads the Hugging Face LLM model for text generation."""
-    HUGGINGFACE_REPO_ID = "mistralai/Mistral-7B-Instruct-v0.3"
-    HF_TOKEN = os.getenv("HF_TOKEN")
-    
-    if not HF_TOKEN:
-        st.error("⚠️ Hugging Face API token is missing. Please check your environment variables.")
-        return None
-    
-    try:
-        return HuggingFaceEndpoint(
-            repo_id=HUGGINGFACE_REPO_ID,
-            task="text-generation",
-            temperature=0.3,
-            model_kwargs={"token": HF_TOKEN, "max_length": 256}
-        )
-    except Exception as e:
-        st.error(f"⚠️ Error loading LLM: {str(e)}")
-        return None
-
-def set_custom_prompt():
-    """Defines the chatbot's behavior with a custom prompt template."""
-    return PromptTemplate(
-        template="""
-        You are an SEO chatbot with advanced knowledge. Answer based **strictly** on the provided documents.
-        
-        If the answer is in the context, provide a **clear, professional, and concise** response with sources.  
-        If the question is **outside the given context**, politely decline:
-        
-        **"I'm sorry, but I can only provide answers based on the available documents."**
-        
-        **Context:** {context}  
-        **Question:** {question}  
-        
-        **Answer:**  
-        """,
-        input_variables=["context", "question"]
-    )
-
-def generate_response(prompt, vectorstore, llm):
-    """Retrieves relevant documents and generates a response from the LLM."""
-    if not vectorstore or not llm:
-        return "❌ Unable to process your request due to initialization issues."
-    
-    try:
-        qa_chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=vectorstore.as_retriever(search_kwargs={'k': 3}),
-            return_source_documents=True,
-            chain_type_kwargs={'prompt': set_custom_prompt()}
-        )
-        
-        response_data = qa_chain.invoke({'query': prompt})
-        result = response_data.get("result", "")
-        source_documents = response_data.get("source_documents", [])
-
-        if not result or not source_documents:
-            return "❌ Sorry, but I can only provide answers based on the available documents."
-
-        formatted_sources = "\n\n📚 **Sources:**" + "".join(
-            [f"\n- {doc.metadata.get('source', 'Unknown')} (Page: {doc.metadata.get('page', 'N/A')})" for doc in source_documents]
-        )
-        return f"{result}{formatted_sources}"
-
-    except Exception as e:
-        return f"⚠️ **Error:** {str(e)}"
-
-def main():
-    """Runs the Streamlit chatbot application."""
-    st.title("🧠 Brainmines SEO Chatbot - Your AI Assistant for SEO Queries 🚀")
-
-    # ✅ Load vector store and LLM
-    vectorstore = get_vectorstore()
-    llm = load_llm()
-
-    if not vectorstore or not llm:
-        st.error("⚠️ Failed to initialize vector store or LLM. Please check configurations.")
-        return
-    
-    # ✅ Initialize session state
-    if "messages" not in st.session_state:
-        st.session_state.messages = [
-            {"role": "assistant", "content": "Hello! 👋 I'm here to assist you with SEO-related queries. 🚀"},
-        ]
-    
-    # ✅ Display chat history
-    for message in st.session_state.messages:
-        st.chat_message(message["role"]).markdown(message["content"])
-    
-    prompt = st.chat_input("💬 Enter your SEO question here")
-
-    if prompt:
-        st.chat_message("user").markdown(prompt)
-        st.session_state.messages.append({"role": "user", "content": prompt})
-
-        with st.spinner("Thinking... 🤔"):
-            response = generate_response(prompt, vectorstore, llm)
-
-        st.chat_message("assistant").markdown(response)
-        st.session_state.messages.append({"role": "assistant", "content": response})
-
-if __name__ == "__main__":
-    main()
+import os
+import streamlit as st
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import PromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint
+from dotenv import load_dotenv, find_dotenv
+
+
+# ✅ Load environment variables
+load_dotenv(find_dotenv())
+
+# ✅ FAISS Database Path
+DB_FAISS_PATH = "vectorstore/db_faiss"
+
+@st.cache_resource
+def get_vectorstore():
+    """Loads the FAISS vector store with embeddings."""
+    try:
+        embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+        return FAISS.load_local(DB_FAISS_PATH, embedding_model, allow_dangerous_deserialization=True)
+    except Exception as e:
+        st.error(f"⚠️ Error loading vector store: {str(e)}")
+        return None
+
+@st.cache_resource
+def load_llm():
+    """Loads the Hugging Face LLM model for text generation."""
+    HUGGINGFACE_REPO_ID = "mistralai/Mistral-7B-Instruct-v0.3"
+    HF_TOKEN = os.getenv("HF_TOKEN")
+    
+    if not HF_TOKEN:
+        st.error("⚠️ Hugging Face API token is missing. Please check your environment variables.")
+        return None
+    
+    try:
+        return HuggingFaceEndpoint(
+            repo_id=HUGGINGFACE_REPO_ID,
+            task="text-generation",
+            temperature=0.3,
+            model_kwargs={"token": HF_TOKEN, "max_length": 256}
+        )
+    except Exception as e:
+        st.error(f"⚠️ Error loading LLM: {str(e)}")
+        return None
+
+def set_custom_prompt():
+    """Defines the chatbot's behavior with a custom prompt template."""
+    return PromptTemplate(
+        template="""
+        You are an SEO chatbot with advanced knowledge. Answer based **strictly** on the provided documents.
+        
+        If the answer is in the context, provide a **clear, professional, and concise** response with sources.  
+        If the question is **outside the given context**, politely decline:
+        
+        **"I'm sorry, but I can only provide answers based on the available documents."**
+        
+        **Context:** {context}  
+        **Question:** {question}  
+        
+        **Answer:**  
+        """,
+        input_variables=["context", "question"]
+    )
+
+def generate_response(prompt, vectorstore, llm):
+    """Retrieves relevant documents and generates a response from the LLM."""
+    if not vectorstore or not llm:
+        return "❌ Unable to process your request due to initialization issues."
+    
+    try:
+        qa_chain = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=vectorstore.as_retriever(search_kwargs={'k': 3}),
+            return_source_documents=True,
+            chain_type_kwargs={'prompt': set_custom_prompt()}
+        )
+        
+        response_data = qa_chain.invoke({'query': prompt})
+        result = response_data.get("result", "")
+        source_documents = response_data.get("source_documents", [])
+
+        if not result or not source_documents:
+            return "❌ Sorry, but I can only provide answers based on the available documents."
+
+        formatted_sources = "\n\n📚 **Sources:**" + "".join(
+            [f"\n- {doc.metadata.get('source', 'Unknown')} (Page: {doc.metadata.get('page', 'N/A')})" for doc in source_documents]
+        )
+        return f"{result}{formatted_sources}"
+
+    except Exception as e:
+        return f"⚠️ **Error:** {str(e)}"
+
+def main():
+    """Runs the Streamlit chatbot application."""
+    st.title("🧠 Brainmines SEO Chatbot - Your AI Assistant for SEO Queries 🚀")
+
+    # ✅ Load vector store and LLM
+    vectorstore = get_vectorstore()
+    llm = load_llm()
+
+    if not vectorstore or not llm:
+        st.error("⚠️ Failed to initialize vector store or LLM. Please check configurations.")
+        return
+    
+    # ✅ Initialize session state
+    if "messages" not in st.session_state:
+        st.session_state.messages = [
+            {"role": "assistant", "content": "Hello! 👋 I'm here to assist you with SEO-related queries. 🚀"},
+        ]
+    
+    # ✅ Display chat history
+    for message in st.session_state.messages:
+        st.chat_message(message["role"]).markdown(message["content"])
+    
+    prompt = st.chat_input("💬 Enter your SEO question here")
+
+    if prompt:
+        st.chat_message("user").markdown(prompt)
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        with st.spinner("Thinking... 🤔"):
+            response = generate_response(prompt, vectorstore, llm)
+
+        st.chat_message("assistant").markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+if __name__ == "__main__":
+    main()