Update app.py

app.py

@@ -1,119 +1,95 @@
-from pypdf import PdfReader
-from langchain.document_loaders import PyPDFLoader
-from langchain.document_loaders import TextLoader
-from langchain.document_loaders import Docx2txtLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_text_splitters import RecursiveCharacterTextSplitter
+import os  
+import streamlit as st  
+import google.generativeai as genai  
+from langchain_google_genai import GoogleGenerativeAIEmbeddings  
+from langchain_google_genai import ChatGoogleGenerativeAI  
+from langchain_community.document_loaders import PyPDFLoader   
+from langchain.text_splitter import RecursiveCharacterTextSplitter  
+from langchain_community.vectorstores import Chroma  
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder  
+from langchain_core.messages import HumanMessage, SystemMessage  
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain 
+from langchain.chains.combine_documents import create_stuff_documents_chain 
+from dotenv import load_dotenv  
 from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Chroma
-from huggingface_hub import notebook_login
-import torch
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.messages import HumanMessage, SystemMessage
-from langchain.chains import create_history_aware_retriever, create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from dotenv import load_dotenv, dotenv_values
-from langchain.schema.document import Document
+
+from sentence_transformers import SentenceTransformer
+
+import pysqlite3
+import sys
+sys.modules['sqlite3'] = pysqlite3
+
 import os
-import streamlit as st
-HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-os.environ['HUGGINGFACEHUB_API_TOKEN'] = HUGGINGFACEHUB_API_TOKEN
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
 
+# Retrieve Google API key
+GOOGLE_API_KEY = "AIzaSyAytkzRS0Xp0pCyo6WqKJ4m1o330bF-gPk"
 
 if not GOOGLE_API_KEY:
     raise ValueError("Gemini API key not found. Please set it in the .env file.")
 
-st.set_page_config(page_title="English Tutor Chatbot", layout="centered")
-st.title("English Turtor Bot")
+os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
+
+# Streamlit app configuration
+st.set_page_config(page_title="English Chatbot", layout="centered")
+st.title("English Tutor Bot")
 
-# Initialize OpenAI LLM 
+# Initialize Google Generative AI LLM 
 llm = ChatGoogleGenerativeAI(
     model="gemini-1.5-pro-latest",
-    temperature=0.2,  # Slightly higher for varied responses
+    temperature=0.2,
     max_tokens=None,
     timeout=None,
     max_retries=2,
 )
 
-
-embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
+# Initialize embeddings using HuggingFace
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 
 def load_preprocessed_vectorstore():
     try:
-        reader = PdfReader("sound.pdf")
-        num_pages = len(reader.pages)
-        
-        document = []
-        dict1 = {}
-        indx = 0
-        for i in range(34,456):
-            page = reader.pages[i]
-            document.append(Document(page_content=page.extract_text()))
-            text_splitter = RecursiveCharacterTextSplitter(
+        loader = PyPDFLoader("sound.pdf")
+        documents = loader.load()
+
+        text_splitter = RecursiveCharacterTextSplitter(
             separators=["\n\n", "\n", ". ", " ", ""],
             chunk_size=3000, 
-            chunk_overlap=1000)
-        
-            document_chunks = text_splitter.split_documents(document)
+            chunk_overlap=1000
+        )
+
+        document_chunks = text_splitter.split_documents(documents)
 
-        vector_store =Chroma.from_documents(document_chunks,embedding=embeddings, persist_directory='./data')
+        vector_store = Chroma.from_documents(
+            embedding=embeddings,
+            documents=document_chunks,
+            persist_directory="./data32"
+        )
         return vector_store
     except Exception as e:
         st.error(f"Error creating vector store: {e}")
         return None
 
 def get_context_retriever_chain(vector_store):
-    """Creates a history-aware retriever chain."""
     retriever = vector_store.as_retriever()
 
-    # Define the prompt for the retriever chain
     prompt = ChatPromptTemplate.from_messages([
         MessagesPlaceholder(variable_name="chat_history"),
         ("human", "{input}"),
-        ("system", """Given the chat history and the latest user question, which might reference context in the chat history,
-formulate a standalone question that can be understood without the chat history.
-If the question is directly addressed within the provided document, provide a relevant answer.
-If the question is not explicitly addressed in the document, return the following message:
-'This question is beyond the scope of the available information. Please contact the mentor for further assistance.'
-Do NOT answer the question directly, just reformulate it if needed and otherwise return it as is.
-Also if the questions is about any irrelevent topic like politics, war, homosexuality, transgender etc just reply the following message:
-'Please reframe your question'""")
+        ("system", """Given the chat history and the latest user question, which might reference context in the chat history, Answer the question
+        by taking reference from the document.
+If the question is directly addressed within the provided document, provide a relevant answer. 
+If the question is not explicitly addressed in the document, return the following message: 
+'This question is beyond the scope of the available information. Please contact your mentor for further assistance.'
+Do NOT answer the question directly, just reformulate it if needed and otherwise return it as is.""")
     ])
 
     retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
     return retriever_chain
 
 def get_conversational_chain(retriever_chain):
-    """Creates a conversational chain using the retriever chain."""
     prompt = ChatPromptTemplate.from_messages([
-        ("system", """I am an advanced English tutor designed to help users improve their English language skills through interactive lessons, personalized feedback, and quizzes. Your goal is to enhance their vocabulary, grammar, reading comprehension, and speaking ability. You should adapt to their skill level and learning preferences."
-
-User Interaction Guidelines:
-
-Learning Mode: Begin by asking the user their current proficiency level (Beginner, Intermediate, Advanced) and their learning goals (e.g., improve vocabulary, master grammar, prepare for an exam, etc.).
-Interactive Lessons: Provide engaging lessons tailored to their level. Use examples, simple explanations, and interactive questions. For example:
-Beginner: "What's the plural form of 'apple'?"
-Advanced: "Explain the difference between 'affect' and 'effect' with examples."
-Quizzes: Offer quizzes after lessons to reinforce learning. Use multiple-choice, fill-in-the-blank, or open-ended questions. Adjust the difficulty based on user performance. Provide immediate feedback, explaining why an answer is correct or incorrect.
-Speaking Practice: If requested, simulate conversations and provide constructive feedback on grammar, vocabulary, and pronunciation.
-Example Response:
-
-Learning Mode Prompt: "Hello! I'm here to help you improve your English. Could you tell me about your current level and goals? For instance, do you want to focus on grammar, expand your vocabulary, or practice speaking?"
-Interactive Lesson Prompt: "Let's practice forming questions. Convert this statement into a question: 'She is reading a book.'"
-Quiz Prompt: "Choose the correct option: 'The cat ____ on the mat.'
-A) sit
-B) sits
-C) sitting
-D) siting"
-(Correct answer: B - 'sits')
-Adaptability Features:
-
-Respond dynamically to user input, simplifying explanations or increasing complexity as needed.
-Encourage and motivate users by celebrating their progress.
-Offer follow-up suggestions after quizzes, e.g., "You did well on vocabulary! Shall we try a grammar-focused session next?
+        ("system", """Hello! I'm your English Tutor, I am here to help you with learning english and can also take quiz to test your skills.
+Note: I will only provide information that is available within our database to ensure accuracy. Let's get started!
 """
          "\n\n"
          "{context}"),
@@ -127,19 +103,19 @@ Offer follow-up suggestions after quizzes, e.g., "You did well on vocabulary! Sh
 def get_response(user_query):
     retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
     conversation_rag_chain = get_conversational_chain(retriever_chain)
-    
+
     formatted_chat_history = []
     for message in st.session_state.chat_history:
         if isinstance(message, HumanMessage):
             formatted_chat_history.append({"author": "user", "content": message.content})
         elif isinstance(message, SystemMessage):
             formatted_chat_history.append({"author": "assistant", "content": message.content})
-    
+
     response = conversation_rag_chain.invoke({
         "chat_history": formatted_chat_history,
         "input": user_query
     })
-    
+
     return response['answer']
 
 # Load the preprocessed vector store from the local directory
@@ -148,7 +124,7 @@ st.session_state.vector_store = load_preprocessed_vectorstore()
 # Initialize chat history if not present
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = [
-        {"author": "assistant", "content": "Hello, I am an English Tutor. How can I help you?"}
+        {"author": "assistant", "content": "Hello, I am Precollege. How can I help you?"}
     ]
 
 # Main app logic
@@ -167,7 +143,7 @@ else:
 
     # Add user input box below the chat
     with st.container():
-        with st.form(key="chat_form"):
+        with st.form(key="chat_form", clear_on_submit=True):
             user_query = st.text_input("Type your message here...", key="user_input")
             submit_button = st.form_submit_button("Send")
 
@@ -178,7 +154,4 @@ else:
             st.session_state.chat_history.append({"author": "assistant", "content": response})
 
             # Rerun the app to refresh the chat display
-            st.rerun()
-
-
-""""""
+            st.rerun()