Upload 5 files

.env

@@ -0,0 +1 @@
+GROQ_API_KEY=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

.gitignore

@@ -0,0 +1,84 @@
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# VS Code
+.vscode/
+*.code-workspace
+
+# Environment variables
+.env
+
+# Model files and vectors
+vectorDB/
+embeddings/
+*.bin
+*.pkl
+*.h5
+*.faiss
+*.index
+
+# Logs
+*.log
+logs/
+log/
+
+# Data
+data/
+*.csv
+*.json
+*.xlsx
+
+# Operating System
+.DS_Store
+Thumbs.db
+
+# IDE specific files
+.idea/
+*.swp
+*.swo
+.spyderproject
+.spyproject
+
+# Large media files
+*.jpg
+*.jpeg
+*.png
+*.gif
+*.pdf
+*.mp4
+*.mov
+
+# Local development settings
+local_settings.py
+settings_local.py

app.py

@@ -0,0 +1,115 @@
+import streamlit as st
+from environs import Env
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.output_parsers import StrOutputParser
+import time
+
+# Load resources and initialize environment
+def load_resources():
+    parser = StrOutputParser()
+    env = Env()
+    env.read_env(".env")
+
+    api_key = env("GROQ_API_KEY")
+    chat = ChatGroq(temperature=0.4, model_name="mixtral-8x7b-32768")
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+    try:
+        vector_store = FAISS.load_local("vectorDB", embeddings, allow_dangerous_deserialization=True)
+    except Exception as e:
+        st.error(f"Error loading vector store: {e}")
+        return None, None
+
+    template = """
+    You are 'AskIIIT', a reliable and trustworthy AI assistant specifically designed 
+    to answer questions about IIITDMJ, developed by Prince Deepak Siddharth,
+    who is an undergrad at IIITDMJ pursuing BTech in CSE of 2023 batch.
+    Your responses must be strictly based on the provided context.
+
+    - Do not provide information beyond the context.
+    - If the context does not cover the question, respond with: 
+      "I don't have enough information about this. Please visit www.iiitdmj.ac.in for more details."
+    - Avoid assumptions, speculations, or hallucinations.
+
+    Ensure clarity, accuracy, and relevance in your responses.
+
+    Context: {context}
+
+    Question: {question}
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+    llm_chain = prompt | chat | parser
+    return llm_chain, vector_store
+
+# Function to get assistant response
+def get_assistant_response(user_query, vector_store, llm_chain):
+    retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+    retrieved_docs = retriever.invoke(user_query)
+    context = "\n\n".join([doc.page_content for doc in retrieved_docs])
+    input_data = {"context": context, "question": user_query}
+    assistant_response = llm_chain.invoke(input_data)
+    return assistant_response
+
+# Function to display typing animation for assistant response
+def display_typing_animation(response_text):
+    response_placeholder = st.empty()
+    typing_speed = 00.01  # Adjust typing speed as needed
+    displayed_text = ""
+
+    for char in response_text:
+        displayed_text += char
+        response_placeholder.write(displayed_text)
+        time.sleep(typing_speed)
+
+    return response_placeholder
+
+# Load resources
+llm_chain, vector_store = load_resources()
+
+# Streamlit UI
+st.set_page_config(
+    page_title="AskIIIT", 
+    layout="centered"
+)
+
+# Add the IIITDMJ logo and heading
+logo_path = "photo\iiitdmjLOGO.jpeg"  # Replace with the path to your logo file
+col1, col2 = st.columns([1, 4])  # Adjust column widths as needed
+with col1:
+    st.image(logo_path, width=50)  # Adjust width as per your requirement
+with col2:
+    st.title("AskIIIT")
+    st.caption("Your AI-Powered IIITDMJ Knowledge Companion")
+
+if vector_store is None:
+    st.error("Failed to load vector store. Please check your setup.")
+else:
+    # Initialize session state for chat history
+    if "chat_history" not in st.session_state:
+        st.session_state["chat_history"] = []
+
+    # Display previous chat history
+    for user_message, assistant_message in st.session_state["chat_history"]:
+        st.chat_message("user").write(user_message)
+        st.chat_message("assistant").write(assistant_message)
+
+    # Input field for user queries
+    user_query = st.chat_input("Ask me anything about IIITDMJ!")
+    if user_query:
+        # Immediately show the user's query in the chat
+        st.chat_message("user").write(user_query)
+
+        # Add a placeholder for assistant's response
+        assistant_placeholder = st.chat_message("assistant").empty()
+
+        # Get assistant response
+        assistant_response = get_assistant_response(user_query, vector_store, llm_chain)
+
+        # Show assistant's response with typing animation
+        display_typing_animation(assistant_response)
+
+        # Add the query and response to the chat history
+        st.session_state["chat_history"].append((user_query, assistant_response))

requirements.txt

@@ -0,0 +1,15 @@
+langchain>=0.1.0
+langchain-core>=0.1.0
+langchain-community>=0.1.0
+langchain-groq>=0.1.0
+langchain-huggingface>=0.1.0
+streamlit>=1.30.0
+environs>=9.5.0
+faiss-cpu>=1.7.4
+sentence-transformers>=2.2.2
+torch>=2.1.0
+transformers>=4.36.0
+gradio>=4.0.0
+python-dotenv>=1.0.0
+tqdm>=4.66.1
+ipywidgets>=8.0.0