Update pages/exam_prepration.py

pages/exam_prepration.py

@@ -1,325 +1,325 @@
-import streamlit as st
-import random
-import time
-from typing import List, Dict
-from langchain_openai import ChatOpenAI
-from langchain.schema import HumanMessage, SystemMessage
-from langchain_community.document_loaders import PyPDFLoader, TextLoader, UnstructuredWordDocumentLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain_community.graphs import NetworkxEntityGraph
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-import os
-from dotenv import load_dotenv
-import requests
-from bs4 import BeautifulSoup
-
-# Load environment variables
-load_dotenv()
-
-AI71_BASE_URL = "https://api.ai71.ai/v1/"
-AI71_API_KEY = "api71-api-92fc2ef9-9f3c-47e5-a019-18e257b04af2"
-GOOGLE_API_KEY = "AIzaSyD-1OMuZ0CxGAek0PaXrzHOmcDWFvZQtm8"
-GOOGLE_CSE_ID = "877170db56f5c4629"
-YOUTUBE_API_KEY = "AIzaSyD-1OMuZ0CxGAek0PaXrzHOmcDWFvZQtm8"
-
-# Initialize the Falcon model
-chat = ChatOpenAI(
-    model="tiiuae/falcon-180B-chat",
-    api_key=AI71_API_KEY,
-    base_url=AI71_BASE_URL,
-    streaming=True,
-)
-
-# Initialize embeddings
-embeddings = HuggingFaceEmbeddings()
-
-FIELDS = [
-    "Mathematics", "Physics", "Chemistry", "Biology", "Computer Science",
-    "History", "Geography", "Literature", "Philosophy", "Psychology",
-    "Sociology", "Economics", "Business", "Finance", "Accounting",
-    "Law", "Political Science", "Environmental Science", "Astronomy", "Geology",
-    "Linguistics", "Anthropology", "Art History", "Music Theory", "Film Studies",
-    "Medical Science", "Nursing", "Public Health", "Nutrition", "Physical Education",
-    "Engineering", "Architecture", "Urban Planning", "Agriculture", "Veterinary Science",
-    "Oceanography", "Meteorology", "Statistics", "Data Science", "Artificial Intelligence",
-    "Cybersecurity", "Renewable Energy", "Quantum Physics", "Neuroscience", "Genetics",
-    "Biotechnology", "Nanotechnology", "Robotics", "Space Exploration", "Cryptography"
-]
-
-# List of educational resources
-EDUCATIONAL_RESOURCES = [
-    "https://www.coursera.org",
-    "https://www.khanacademy.org",
-    "https://scholar.google.com",
-    "https://www.edx.org",
-    "https://www.udacity.com",
-    "https://www.udemy.com",
-    "https://www.futurelearn.com",
-    "https://www.lynda.com",
-    "https://www.skillshare.com",
-    "https://www.codecademy.com",
-    "https://www.brilliant.org",
-    "https://www.duolingo.com",
-    "https://www.ted.com/talks",
-    "https://ocw.mit.edu",
-    "https://www.open.edu/openlearn",
-    "https://www.coursebuffet.com",
-    "https://www.academicearth.org",
-    "https://www.edutopia.org",
-    "https://www.saylor.org",
-    "https://www.openculture.com",
-    "https://www.gutenberg.org",
-    "https://www.archive.org",
-    "https://www.wolframalpha.com",
-    "https://www.quizlet.com",
-    "https://www.mathway.com",
-    "https://www.symbolab.com",
-    "https://www.lessonplanet.com",
-    "https://www.teacherspayteachers.com",
-    "https://www.brainpop.com",
-    "https://www.ck12.org"
-]
-
-def search_web(query: str, num_results: int = 30, max_retries: int = 3) -> List[Dict[str, str]]:
-    user_agents = [
-        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
-        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15',
-        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.101 Safari/537.36'
-    ]
-    
-    for attempt in range(max_retries):
-        try:
-            headers = {'User-Agent': random.choice(user_agents)}
-            service = build("customsearch", "v1", developerKey=GOOGLE_API_KEY)
-            res = service.cse().list(q=query, cx=GOOGLE_CSE_ID, num=num_results).execute()
-            
-            results = []
-            if "items" in res:
-                for item in res["items"]:
-                    result = {
-                        "title": item["title"],
-                        "link": item["link"],
-                        "snippet": item.get("snippet", "")
-                    }
-                    results.append(result)
-            
-            return results
-        except Exception as e:
-            print(f"An error occurred: {e}. Attempt {attempt + 1} of {max_retries}")
-            time.sleep(2 ** attempt)
-    
-    print("Max retries reached. No results found.")
-    return []
-
-def scrape_webpage(url: str) -> str:
-    try:
-        response = requests.get(url, timeout=10)
-        soup = BeautifulSoup(response.content, 'html.parser')
-        return soup.get_text()
-    except Exception as e:
-        print(f"Error scraping {url}: {e}")
-        return ""
-
-def process_documents(uploaded_files):
-    documents = []
-    for uploaded_file in uploaded_files:
-        file_extension = os.path.splitext(uploaded_file.name)[1].lower()
-        
-        if file_extension == '.pdf':
-            loader = PyPDFLoader(uploaded_file)
-        elif file_extension in ['.txt', '.md']:
-            loader = TextLoader(uploaded_file)
-        elif file_extension in ['.doc', '.docx']:
-            loader = UnstructuredWordDocumentLoader(uploaded_file)
-        else:
-            st.warning(f"Unsupported file type: {file_extension}")
-            continue
-        
-        documents.extend(loader.load())
-    
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    texts = text_splitter.split_documents(documents)
-    
-    vectorstore = FAISS.from_documents(texts, embeddings)
-    graph = NetworkxEntityGraph()
-    graph.add_documents(texts)
-    
-    retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
-    
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=chat,
-        chain_type="stuff",
-        retriever=retriever,
-        return_source_documents=True
-    )
-    
-    return qa_chain, graph
-
-def generate_questions(topic, difficulty, num_questions, include_answers, qa_chain=None, graph=None):
-    system_prompt = f"""You are an expert exam question generator. Generate {num_questions} {difficulty}-level questions about {topic}. 
-    {"Each question should be followed by its correct answer." if include_answers else "Do not include answers."}
-    Format your response as follows:
-
-    Q1. [Question]
-    {"A1. [Answer]" if include_answers else ""}
-
-    Q2. [Question]
-    {"A2. [Answer]" if include_answers else ""}
-
-    ... and so on.
-    """
-    
-    if qa_chain and graph:
-        context = graph.get_relevant_documents(topic)
-        context_text = "\n".join([doc.page_content for doc in context])
-        
-        result = qa_chain({"query": system_prompt, "context": context_text})
-        questions = result['result']
-    else:
-        messages = [
-            SystemMessage(content=system_prompt),
-            HumanMessage(content=f"Please generate {num_questions} {difficulty} questions about {topic}.")
-        ]
-        questions = chat(messages).content
-    
-    return questions
-
-def gather_resources(field: str) -> List[Dict[str, str]]:
-    resources = []
-    for resource_url in EDUCATIONAL_RESOURCES:
-        search_results = search_web(f"site:{resource_url} {field}", num_results=1)
-        if search_results:
-            result = search_results[0]
-            content = scrape_webpage(result['link'])
-            resources.append({
-                "title": result['title'],
-                "link": result['link'],
-                "content": content[:500] + "..." if len(content) > 500 else content
-            })
-    
-    # YouTube search
-    youtube = build('youtube', 'v3', developerKey=YOUTUBE_API_KEY)
-    youtube_results = youtube.search().list(q=field, type='video', part='id,snippet', maxResults=5).execute()
-    for item in youtube_results.get('items', []):
-        video_id = item['id']['videoId']
-        resources.append({
-            "title": item['snippet']['title'],
-            "link": f"https://www.youtube.com/watch?v={video_id}",
-            "content": item['snippet']['description'],
-            "thumbnail": item['snippet']['thumbnails']['medium']['url']
-        })
-    
-    return resources
-
-def main():
-    st.set_page_config(page_title="Advanced Exam Preparation System", layout="wide")
-    
-    st.sidebar.title("Advanced Exam Prep")
-    st.sidebar.markdown("""
-    Welcome to our advanced exam preparation system! 
-    Here you can generate practice questions, explore educational resources, 
-    and interact with an AI tutor to enhance your learning experience.
-    """)
-    
-    # Main area tabs
-    tab1, tab2, tab3 = st.tabs(["Question Generator", "Resource Explorer", "Academic Tutor"])
-    
-    with tab1:
-        st.header("Question Generator")
-        col1, col2 = st.columns(2)
-        with col1:
-            topic = st.text_input("Enter the exam topic:")
-            exam_type = st.selectbox("Select exam type:", ["General", "STEM", "Humanities", "Business", "Custom"])
-        with col2:
-            difficulty = st.select_slider(
-                "Select difficulty level:",
-                options=["Super Easy", "Easy", "Beginner", "Intermediate", "Higher Intermediate", "Master", "Advanced"]
-            )
-            num_questions = st.number_input("Number of questions:", min_value=1, max_value=50, value=5)
-        include_answers = st.checkbox("Include answers", value=True)
-        
-        if st.button("Generate Questions", key="generate_questions"):
-            if topic:
-                with st.spinner("Generating questions..."):
-                    questions = generate_questions(topic, difficulty, num_questions, include_answers)
-                st.success("Questions generated successfully!")
-                st.markdown(questions)
-            else:
-                st.warning("Please enter a topic.")
-    
-    with tab2:
-        st.header("Resource Explorer")
-        selected_field = st.selectbox("Select a field to explore:", FIELDS)
-        if st.button("Explore Resources", key="explore_resources"):
-            with st.spinner("Gathering resources..."):
-                resources = gather_resources(selected_field)
-            st.success(f"Found {len(resources)} resources!")
-            
-            for i, resource in enumerate(resources):
-                col1, col2 = st.columns([1, 3])
-                with col1:
-                    if "thumbnail" in resource:
-                        st.image(resource["thumbnail"], use_column_width=True)
-                    else:
-                        st.image("https://via.placeholder.com/150", use_column_width=True)
-                with col2:
-                    st.subheader(f"[{resource['title']}]({resource['link']})")
-                    st.write(resource['content'])
-                st.markdown("---")
-    
-    with tab3:
-        st.header("Academic Tutor")
-        uploaded_files = st.file_uploader("Upload documents (PDF, TXT, MD, DOC, DOCX)", type=["pdf", "txt", "md", "doc", "docx"], accept_multiple_files=True)
-        
-        if uploaded_files:
-            qa_chain, graph = process_documents(uploaded_files)
-            st.success("Documents processed successfully!")
-        else:
-            qa_chain, graph = None, None
-        
-        st.subheader("Chat with AI Tutor")
-        if 'chat_history' not in st.session_state:
-            st.session_state.chat_history = []
-
-        chat_container = st.container()
-        with chat_container:
-            for i, (role, message) in enumerate(st.session_state.chat_history):
-                with st.chat_message(role):
-                    st.write(message)
-
-        user_input = st.chat_input("Ask a question or type 'search: your query' to perform a web search:")
-        if user_input:
-            st.session_state.chat_history.append(("user", user_input))
-            with st.chat_message("user"):
-                st.write(user_input)
-
-            with st.chat_message("assistant"):
-                if user_input.lower().startswith("search:"):
-                    search_query = user_input[7:].strip()
-                    search_results = search_web(search_query, num_results=3)
-                    response = f"Here are some search results for '{search_query}':\n\n"
-                    for result in search_results:
-                        response += f"- [{result['title']}]({result['link']})\n  {result['snippet']}\n\n"
-                else:
-                    response = chat([HumanMessage(content=user_input)]).content
-                st.write(response)
-                st.session_state.chat_history.append(("assistant", response))
-
-        # Scroll to bottom of chat
-        js = f"""
-        <script>
-            function scroll_to_bottom() {{
-                var chatElement = window.parent.document.querySelector('.stChatFloatingInputContainer');
-                chatElement.scrollIntoView({{behavior: 'smooth'}});
-            }}
-            scroll_to_bottom();
-        </script>
-        """
-        st.components.v1.html(js)
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import random
+import time
+from typing import List, Dict
+from langchain_community.chat_models import ChatOpenAI
+from langchain.schema import HumanMessage, SystemMessage
+from langchain_community.document_loaders import PyPDFLoader, TextLoader, UnstructuredWordDocumentLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain_community.graphs import NetworkxEntityGraph
+from googleapiclient.discovery import build
+from googleapiclient.errors import HttpError
+import os
+from dotenv import load_dotenv
+import requests
+from bs4 import BeautifulSoup
+
+# Load environment variables
+load_dotenv()
+
+AI71_BASE_URL = "https://api.ai71.ai/v1/"
+AI71_API_KEY = "api71-api-92fc2ef9-9f3c-47e5-a019-18e257b04af2"
+GOOGLE_API_KEY = "AIzaSyD-1OMuZ0CxGAek0PaXrzHOmcDWFvZQtm8"
+GOOGLE_CSE_ID = "877170db56f5c4629"
+YOUTUBE_API_KEY = "AIzaSyD-1OMuZ0CxGAek0PaXrzHOmcDWFvZQtm8"
+
+# Initialize the Falcon model
+chat = ChatOpenAI(
+    model="tiiuae/falcon-180B-chat",
+    api_key=AI71_API_KEY,
+    base_url=AI71_BASE_URL,
+    streaming=True,
+)
+
+# Initialize embeddings
+embeddings = HuggingFaceEmbeddings()
+
+FIELDS = [
+    "Mathematics", "Physics", "Chemistry", "Biology", "Computer Science",
+    "History", "Geography", "Literature", "Philosophy", "Psychology",
+    "Sociology", "Economics", "Business", "Finance", "Accounting",
+    "Law", "Political Science", "Environmental Science", "Astronomy", "Geology",
+    "Linguistics", "Anthropology", "Art History", "Music Theory", "Film Studies",
+    "Medical Science", "Nursing", "Public Health", "Nutrition", "Physical Education",
+    "Engineering", "Architecture", "Urban Planning", "Agriculture", "Veterinary Science",
+    "Oceanography", "Meteorology", "Statistics", "Data Science", "Artificial Intelligence",
+    "Cybersecurity", "Renewable Energy", "Quantum Physics", "Neuroscience", "Genetics",
+    "Biotechnology", "Nanotechnology", "Robotics", "Space Exploration", "Cryptography"
+]
+
+# List of educational resources
+EDUCATIONAL_RESOURCES = [
+    "https://www.coursera.org",
+    "https://www.khanacademy.org",
+    "https://scholar.google.com",
+    "https://www.edx.org",
+    "https://www.udacity.com",
+    "https://www.udemy.com",
+    "https://www.futurelearn.com",
+    "https://www.lynda.com",
+    "https://www.skillshare.com",
+    "https://www.codecademy.com",
+    "https://www.brilliant.org",
+    "https://www.duolingo.com",
+    "https://www.ted.com/talks",
+    "https://ocw.mit.edu",
+    "https://www.open.edu/openlearn",
+    "https://www.coursebuffet.com",
+    "https://www.academicearth.org",
+    "https://www.edutopia.org",
+    "https://www.saylor.org",
+    "https://www.openculture.com",
+    "https://www.gutenberg.org",
+    "https://www.archive.org",
+    "https://www.wolframalpha.com",
+    "https://www.quizlet.com",
+    "https://www.mathway.com",
+    "https://www.symbolab.com",
+    "https://www.lessonplanet.com",
+    "https://www.teacherspayteachers.com",
+    "https://www.brainpop.com",
+    "https://www.ck12.org"
+]
+
+def search_web(query: str, num_results: int = 30, max_retries: int = 3) -> List[Dict[str, str]]:
+    user_agents = [
+        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
+        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15',
+        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.101 Safari/537.36'
+    ]
+    
+    for attempt in range(max_retries):
+        try:
+            headers = {'User-Agent': random.choice(user_agents)}
+            service = build("customsearch", "v1", developerKey=GOOGLE_API_KEY)
+            res = service.cse().list(q=query, cx=GOOGLE_CSE_ID, num=num_results).execute()
+            
+            results = []
+            if "items" in res:
+                for item in res["items"]:
+                    result = {
+                        "title": item["title"],
+                        "link": item["link"],
+                        "snippet": item.get("snippet", "")
+                    }
+                    results.append(result)
+            
+            return results
+        except Exception as e:
+            print(f"An error occurred: {e}. Attempt {attempt + 1} of {max_retries}")
+            time.sleep(2 ** attempt)
+    
+    print("Max retries reached. No results found.")
+    return []
+
+def scrape_webpage(url: str) -> str:
+    try:
+        response = requests.get(url, timeout=10)
+        soup = BeautifulSoup(response.content, 'html.parser')
+        return soup.get_text()
+    except Exception as e:
+        print(f"Error scraping {url}: {e}")
+        return ""
+
+def process_documents(uploaded_files):
+    documents = []
+    for uploaded_file in uploaded_files:
+        file_extension = os.path.splitext(uploaded_file.name)[1].lower()
+        
+        if file_extension == '.pdf':
+            loader = PyPDFLoader(uploaded_file)
+        elif file_extension in ['.txt', '.md']:
+            loader = TextLoader(uploaded_file)
+        elif file_extension in ['.doc', '.docx']:
+            loader = UnstructuredWordDocumentLoader(uploaded_file)
+        else:
+            st.warning(f"Unsupported file type: {file_extension}")
+            continue
+        
+        documents.extend(loader.load())
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+    
+    vectorstore = FAISS.from_documents(texts, embeddings)
+    graph = NetworkxEntityGraph()
+    graph.add_documents(texts)
+    
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+    
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=chat,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=True
+    )
+    
+    return qa_chain, graph
+
+def generate_questions(topic, difficulty, num_questions, include_answers, qa_chain=None, graph=None):
+    system_prompt = f"""You are an expert exam question generator. Generate {num_questions} {difficulty}-level questions about {topic}. 
+    {"Each question should be followed by its correct answer." if include_answers else "Do not include answers."}
+    Format your response as follows:
+
+    Q1. [Question]
+    {"A1. [Answer]" if include_answers else ""}
+
+    Q2. [Question]
+    {"A2. [Answer]" if include_answers else ""}
+
+    ... and so on.
+    """
+    
+    if qa_chain and graph:
+        context = graph.get_relevant_documents(topic)
+        context_text = "\n".join([doc.page_content for doc in context])
+        
+        result = qa_chain({"query": system_prompt, "context": context_text})
+        questions = result['result']
+    else:
+        messages = [
+            SystemMessage(content=system_prompt),
+            HumanMessage(content=f"Please generate {num_questions} {difficulty} questions about {topic}.")
+        ]
+        questions = chat(messages).content
+    
+    return questions
+
+def gather_resources(field: str) -> List[Dict[str, str]]:
+    resources = []
+    for resource_url in EDUCATIONAL_RESOURCES:
+        search_results = search_web(f"site:{resource_url} {field}", num_results=1)
+        if search_results:
+            result = search_results[0]
+            content = scrape_webpage(result['link'])
+            resources.append({
+                "title": result['title'],
+                "link": result['link'],
+                "content": content[:500] + "..." if len(content) > 500 else content
+            })
+    
+    # YouTube search
+    youtube = build('youtube', 'v3', developerKey=YOUTUBE_API_KEY)
+    youtube_results = youtube.search().list(q=field, type='video', part='id,snippet', maxResults=5).execute()
+    for item in youtube_results.get('items', []):
+        video_id = item['id']['videoId']
+        resources.append({
+            "title": item['snippet']['title'],
+            "link": f"https://www.youtube.com/watch?v={video_id}",
+            "content": item['snippet']['description'],
+            "thumbnail": item['snippet']['thumbnails']['medium']['url']
+        })
+    
+    return resources
+
+def main():
+    st.set_page_config(page_title="Advanced Exam Preparation System", layout="wide")
+    
+    st.sidebar.title("Advanced Exam Prep")
+    st.sidebar.markdown("""
+    Welcome to our advanced exam preparation system! 
+    Here you can generate practice questions, explore educational resources, 
+    and interact with an AI tutor to enhance your learning experience.
+    """)
+    
+    # Main area tabs
+    tab1, tab2, tab3 = st.tabs(["Question Generator", "Resource Explorer", "Academic Tutor"])
+    
+    with tab1:
+        st.header("Question Generator")
+        col1, col2 = st.columns(2)
+        with col1:
+            topic = st.text_input("Enter the exam topic:")
+            exam_type = st.selectbox("Select exam type:", ["General", "STEM", "Humanities", "Business", "Custom"])
+        with col2:
+            difficulty = st.select_slider(
+                "Select difficulty level:",
+                options=["Super Easy", "Easy", "Beginner", "Intermediate", "Higher Intermediate", "Master", "Advanced"]
+            )
+            num_questions = st.number_input("Number of questions:", min_value=1, max_value=50, value=5)
+        include_answers = st.checkbox("Include answers", value=True)
+        
+        if st.button("Generate Questions", key="generate_questions"):
+            if topic:
+                with st.spinner("Generating questions..."):
+                    questions = generate_questions(topic, difficulty, num_questions, include_answers)
+                st.success("Questions generated successfully!")
+                st.markdown(questions)
+            else:
+                st.warning("Please enter a topic.")
+    
+    with tab2:
+        st.header("Resource Explorer")
+        selected_field = st.selectbox("Select a field to explore:", FIELDS)
+        if st.button("Explore Resources", key="explore_resources"):
+            with st.spinner("Gathering resources..."):
+                resources = gather_resources(selected_field)
+            st.success(f"Found {len(resources)} resources!")
+            
+            for i, resource in enumerate(resources):
+                col1, col2 = st.columns([1, 3])
+                with col1:
+                    if "thumbnail" in resource:
+                        st.image(resource["thumbnail"], use_column_width=True)
+                    else:
+                        st.image("https://via.placeholder.com/150", use_column_width=True)
+                with col2:
+                    st.subheader(f"[{resource['title']}]({resource['link']})")
+                    st.write(resource['content'])
+                st.markdown("---")
+    
+    with tab3:
+        st.header("Academic Tutor")
+        uploaded_files = st.file_uploader("Upload documents (PDF, TXT, MD, DOC, DOCX)", type=["pdf", "txt", "md", "doc", "docx"], accept_multiple_files=True)
+        
+        if uploaded_files:
+            qa_chain, graph = process_documents(uploaded_files)
+            st.success("Documents processed successfully!")
+        else:
+            qa_chain, graph = None, None
+        
+        st.subheader("Chat with AI Tutor")
+        if 'chat_history' not in st.session_state:
+            st.session_state.chat_history = []
+
+        chat_container = st.container()
+        with chat_container:
+            for i, (role, message) in enumerate(st.session_state.chat_history):
+                with st.chat_message(role):
+                    st.write(message)
+
+        user_input = st.chat_input("Ask a question or type 'search: your query' to perform a web search:")
+        if user_input:
+            st.session_state.chat_history.append(("user", user_input))
+            with st.chat_message("user"):
+                st.write(user_input)
+
+            with st.chat_message("assistant"):
+                if user_input.lower().startswith("search:"):
+                    search_query = user_input[7:].strip()
+                    search_results = search_web(search_query, num_results=3)
+                    response = f"Here are some search results for '{search_query}':\n\n"
+                    for result in search_results:
+                        response += f"- [{result['title']}]({result['link']})\n  {result['snippet']}\n\n"
+                else:
+                    response = chat([HumanMessage(content=user_input)]).content
+                st.write(response)
+                st.session_state.chat_history.append(("assistant", response))
+
+        # Scroll to bottom of chat
+        js = f"""
+        <script>
+            function scroll_to_bottom() {{
+                var chatElement = window.parent.document.querySelector('.stChatFloatingInputContainer');
+                chatElement.scrollIntoView({{behavior: 'smooth'}});
+            }}
+            scroll_to_bottom();
+        </script>
+        """
+        st.components.v1.html(js)
+
+if __name__ == "__main__":
+    main()