Update app.py

app.py

@@ -1,144 +1,143 @@
-import streamlit as st
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from transformers import pipeline
-import time
-
-
-# Initialize session state for vector database
-if 'vector_db' not in st.session_state:
-    # Load and process PDF only once
-    @st.cache_resource
-    def initialize_qa_system():
-        # Load PDF
-        pdf_path = "ai_buddy.pdf"  # Update path accordingly
-        data = PyPDFLoader(pdf_path).load()
-        
-        # Split documents
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=750, chunk_overlap=150)
-        splits = text_splitter.split_documents(data)
-        
-        # Initialize embeddings
-        embeddings = HuggingFaceEmbeddings(
-            model_name="sentence-transformers/all-MiniLM-L6-v2"
-        )
-        
-        # Create vector store
-        vector_db = Chroma.from_documents(
-            documents=splits,
-            embedding=embeddings,
-            persist_directory="vector_db"
-        )
-        
-        # Initialize QA pipeline
-        qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
-        
-        return vector_db, qa_pipeline
-    
-    # Initialize the system
-    st.session_state.vector_db, st.session_state.qa_pipeline = initialize_qa_system()
-
-# Streamlit interface setup
-st.set_page_config(page_title="AI-Buddy Assistant", page_icon="🤖", layout="centered")
-st.title("🤖 AI-Buddy Assistant")
-
-# Custom CSS
-st.markdown("""
-    <style>
-        .stChat {
-            font-size: 1.3rem;
-        }
-        .stTextArea textarea {
-            font-size: 1.2rem;
-        }
-        .stMarkdown {
-            font-size: 1.2rem;
-        }
-        .stButton button {
-            background-color: #ff4b4b;
-            color: white;
-            font-size: 1.2rem;
-            padding: 10px 20px;
-            border-radius: 5px;
-            border: none;
-        }
-        .stButton button:hover {
-            background-color: #ff6565;
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-# Sidebar configuration
-st.sidebar.title("Want to know how AI helps in your profession?")
-professions = ["Software Engineer", "Data Scientist", "Marketing Specialist", 
-               "Financial Analyst", "Teacher", "Doctor", "Project Manager", 
-               "Consultant", "Business Analyst", "Other"]
-fields = ["IT", "Healthcare", "Education", "Finance", "Marketing", 
-          "Engineering", "Sales", "Human Resources", "Consulting", "Other"]
-
-profession = st.sidebar.selectbox("Choose Your Profession", professions)
-field = st.sidebar.selectbox("Choose Your Field/Domain", fields)
-
-if profession == "Other":
-    profession = st.sidebar.text_input("Please specify your profession")
-if field == "Other":
-    field = st.sidebar.text_input("Please specify your field")
-
-description = st.sidebar.text_area("About you", 
-                                 placeholder="Briefly describe your role")
-
-# Function to execute query with streaming response
-def execute_query_with_stream(question):
-    retriever = st.session_state.vector_db.as_retriever()
-    retrieved_docs = retriever.get_relevant_documents(question)
-    
-    if not retrieved_docs:
-        return "No relevant information found."
-        
-    context = " ".join([doc.page_content.strip() for doc in retrieved_docs])
-    response = st.session_state.qa_pipeline(question=question, context=context)
-    
-    return response['answer']
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = [
-        {"role": "assistant", "content": "Hello! How can I assist you today?"}
-    ]
-
-# Handle sidebar submission
-if st.sidebar.button("Get AI Insights"):
-    prompt = f"""
-    My profession is {profession} in the {field} field. 
-    Here's about me: {description}. 
-    How can AI and AI-Buddy help me in my role?
-    """
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-# Chat interface
-if prompt := st.chat_input("Ask me anything about AI-Buddy"):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-# Display chat history and generate responses
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
-
-if st.session_state.messages[-1]["role"] == "user":
-    with st.chat_message("assistant"):
-        with st.spinner("Thinking..."):
-            response = execute_query_with_stream(st.session_state.messages[-1]["content"])
-            
-            # Simulate streaming effect
-            placeholder = st.empty()
-            displayed_response = ""
-            for char in response:
-                displayed_response += char
-                placeholder.write(displayed_response)
-                time.sleep(0.02)
-            
-            st.session_state.messages.append(
-                {"role": "assistant", "content": response}
+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+from transformers import pipeline
+import time
+
+# Set page config as the first Streamlit command
+st.set_page_config(page_title="AI-Buddy Assistant", page_icon="🤖", layout="centered")
+
+# Custom CSS
+st.markdown("""
+    <style>
+        .stChat {
+            font-size: 1.3rem;
+        }
+        .stTextArea textarea {
+            font-size: 1.2rem;
+        }
+        .stMarkdown {
+            font-size: 1.2rem;
+        }
+        .stButton button {
+            background-color: #ff4b4b;
+            color: white;
+            font-size: 1.2rem;
+            padding: 10px 20px;
+            border-radius: 5px;
+            border: none;
+        }
+        .stButton button:hover {
+            background-color: #ff6565;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Cache the QA system initialization
+@st.cache_resource
+def initialize_qa_system():
+    # Load PDF
+    pdf_path = "ai_buddy.pdf"  # Update path accordingly
+    data = PyPDFLoader(pdf_path).load()
+    
+    # Split documents
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=750, chunk_overlap=150)
+    splits = text_splitter.split_documents(data)
+    
+    # Initialize embeddings
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )
+    
+    # Create vector store
+    vector_db = Chroma.from_documents(
+        documents=splits,
+        embedding=embeddings,
+        persist_directory="vector_db"
+    )
+    
+    # Initialize QA pipeline
+    qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+    
+    return vector_db, qa_pipeline
+
+# Initialize session state for vector database and QA pipeline
+if 'vector_db' not in st.session_state:
+    st.session_state.vector_db, st.session_state.qa_pipeline = initialize_qa_system()
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "assistant", "content": "Hello! How can I assist you today?"}
+    ]
+
+st.title("🤖 AI-Buddy Assistant")
+
+# Sidebar configuration
+st.sidebar.title("Want to know how AI helps in your profession?")
+professions = ["Software Engineer", "Data Scientist", "Marketing Specialist", 
+               "Financial Analyst", "Teacher", "Doctor", "Project Manager", 
+               "Consultant", "Business Analyst", "Other"]
+fields = ["IT", "Healthcare", "Education", "Finance", "Marketing", 
+          "Engineering", "Sales", "Human Resources", "Consulting", "Other"]
+
+profession = st.sidebar.selectbox("Choose Your Profession", professions)
+field = st.sidebar.selectbox("Choose Your Field/Domain", fields)
+
+if profession == "Other":
+    profession = st.sidebar.text_input("Please specify your profession")
+if field == "Other":
+    field = st.sidebar.text_input("Please specify your field")
+
+description = st.sidebar.text_area("About you", 
+                                 placeholder="Briefly describe your role")
+
+# Function to execute query with streaming response
+def execute_query_with_stream(question):
+    retriever = st.session_state.vector_db.as_retriever()
+    retrieved_docs = retriever.get_relevant_documents(question)
+    
+    if not retrieved_docs:
+        return "No relevant information found."
+        
+    context = " ".join([doc.page_content.strip() for doc in retrieved_docs])
+    response = st.session_state.qa_pipeline(question=question, context=context)
+    
+    return response['answer']
+
+# Handle sidebar submission
+if st.sidebar.button("Get AI Insights"):
+    prompt = f"""
+    My profession is {profession} in the {field} field. 
+    Here's about me: {description}. 
+    How can AI and AI-Buddy help me in my role?
+    """
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+# Chat interface
+if prompt := st.chat_input("Ask me anything about AI-Buddy"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+# Display chat history and generate responses
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+if st.session_state.messages[-1]["role"] == "user":
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = execute_query_with_stream(st.session_state.messages[-1]["content"])
+            
+            # Simulate streaming effect
+            placeholder = st.empty()
+            displayed_response = ""
+            for char in response:
+                displayed_response += char
+                placeholder.write(displayed_response)
+                time.sleep(0.02)
+            
+            st.session_state.messages.append(
+                {"role": "assistant", "content": response}
             )