Added features

app.py

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+import os
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.document_transformers import BeautifulSoupTransformer
+import streamlit as st
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain.indexes import VectorstoreIndexCreator
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+
+model_id="mistralai/Mistral-7B-Instruct-v0.3"
+
+def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
+    """
+    Returns a language model for HuggingFace inference.
+
+    Parameters:
+    - model_id (str): The ID of the HuggingFace model repository.
+    - max_new_tokens (int): The maximum number of new tokens to generate.
+    - temperature (float): The temperature for sampling from the model.
+
+    Returns:
+    - llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
+    """
+    llm = HuggingFaceEndpoint(
+        repo_id=model_id,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        token = os.getenv("HF_TOKEN")
+    )
+    return llm
+
+
+st.set_page_config(page_title="Website Information Retirever Agent", page_icon="🤗")
+st.title("Website Information Retriever Agent")
+st.markdown(f"*This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input.It uses the model mistralai/Mistral-7B-Instruct-v0.3. You can enter the specific website url and the use the agent to gather information.*")
+
+# Initialize session state for avatars
+if "avatars" not in st.session_state:
+    st.session_state.avatars = {'user': None, 'assistant': None}
+
+# Initialize session state for user text input
+if 'user_text' not in st.session_state:
+    st.session_state.user_text = None
+
+if "sitemap_url" not in st.session_state:
+    st.session_state.sitemap_url = None
+
+# Initialize session state for model parameters
+if "max_response_length" not in st.session_state:
+    st.session_state.max_response_length = 256
+
+if "system_message" not in st.session_state:
+    st.session_state.system_message = "friendly AI conversing with a human user"
+
+if "starter_message" not in st.session_state:
+    st.session_state.starter_message = "Hello, there! How can I help you today?"
+
+
+
+    
+# Sidebar for settings
+with st.sidebar:
+    st.header("System Settings")
+
+    # AI Settings
+    st.session_state.system_message = st.text_area(
+        "System Message", value="You are a friendly AI conversing with a human user."
+    )
+    st.session_state.starter_message = st.text_area(
+        'First AI Message', value="Hello, there! How can I help you today?"
+    )
+
+    # Model Settings
+    st.session_state.max_response_length = st.number_input(
+        "Max Response Length", value=256
+    )
+
+    # Avatar Selection
+    st.markdown("*Select Avatars:*")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.session_state.avatars['assistant'] = st.selectbox(
+            "AI Avatar", options=["🤗", "💬", "🤖"], index=0
+        )
+    with col2:
+        st.session_state.avatars['user'] = st.selectbox(
+            "User Avatar", options=["👤", "👱‍♂️", "👨🏾", "👩", "👧🏾"], index=0
+        )
+    # Reset Chat History
+    reset_history = st.button("Reset Chat History")
+    
+# Initialize or reset chat history
+if "chat_history" not in st.session_state or reset_history:
+    st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
+
+if "sitemap_url" in st.session_state:
+    sitemap_url = st.text_input("URL to the website", value="")   
+
+if sitemap_url:
+    with st.spinner("Processing..."):
+        token = os.getenv("HF_TOKEN")
+        loader = WebBaseLoader([sitemap_url])
+        html = loader.load()
+
+    # Transform
+    # bs_transformer = BeautifulSoupTransformer()
+    # docs_transformed = bs_transformer.transform_documents(html,tags_to_extract=["span"])
+
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=10,
+            add_start_index=True,
+            strip_whitespace=True,
+            separators=["\n\n", "\n", ".", " ", ""],
+        )
+        docs_processed = text_splitter.split_documents(html)
+
+
+    # # Create a vector store based on the crawled data
+    # index = VectorstoreIndexCreator().from_loaders([docs_processed])
+
+
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+        db = FAISS.from_documents(docs_processed, embeddings)
+        retriever = db.as_retriever(search_kwargs={"k": 4})
+
+
+
+
+
+
+def get_response(system_message, chat_history, user_text, 
+                 eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
+    """
+    Generates a response from the chatbot model.
+
+    Args:
+        system_message (str): The system message for the conversation.
+        chat_history (list): The list of previous chat messages.
+        user_text (str): The user's input text.
+        model_id (str, optional): The ID of the HuggingFace model to use.
+        eos_token_id (list, optional): The list of end-of-sentence token IDs.
+        max_new_tokens (int, optional): The maximum number of new tokens to generate.
+        get_llm_hf_kws (dict, optional): Additional keyword arguments for the get_llm_hf function.
+
+    Returns:
+        tuple: A tuple containing the generated response and the updated chat history.
+    """
+    # Set up the model
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
+
+    # Create the prompt template
+    prompt = PromptTemplate.from_template(
+        (
+            "[INST] {system_message}"
+            "\nCurrent Conversation:\n{chat_history}\n\n"
+            "\nUser: {user_text}.\n [/INST]"
+            "\nAI:"
+        )
+    )
+    # Make the chain and bind the prompt
+    chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
+    qa = RetrievalQA.from_chain_type(llm=hf, chain_type="refine", retriever=retriever, return_source_documents=False)
+    # Generate the response
+
+    response  = qa.run({"query": user_text})
+    # response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
+    # response = response.split("AI:")[-1]
+    # Update the chat history
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': response})
+    return response, chat_history
+
+
+
+
+    
+
+
+
+
+# Chat interface
+
+if sitemap_url:
+    chat_interface = st.container(border=True)
+    with chat_interface:
+        output_container = st.container()
+        st.session_state.user_text = st.chat_input(placeholder="Enter your text here.")
+        
+    # Display chat messages
+    with output_container:
+        # For every message in the history
+        for message in st.session_state.chat_history:
+            # Skip the system message
+            if message['role'] == 'system':
+                continue
+                
+            # Display the chat message using the correct avatar
+            with st.chat_message(message['role'], 
+                                avatar=st.session_state['avatars'][message['role']]):
+                st.markdown(message['content'])
+                
+ # When the user enter new text:
+    if st.session_state.user_text:
+        
+        # Display the user's new message immediately
+        with st.chat_message("user", 
+                             avatar=st.session_state.avatars['user']):
+            st.markdown(st.session_state.user_text)
+            
+        # Display a spinner status bar while waiting for the response
+        with st.chat_message("assistant", 
+                             avatar=st.session_state.avatars['assistant']):
+
+            with st.spinner("Thinking..."):
+                # Call the Inference API with the system_prompt, user text, and history
+                
+               
+                response, st.session_state.chat_history = get_response(
+                    system_message=st.session_state.system_message, 
+                    user_text=st.session_state.user_text,
+                    chat_history=st.session_state.chat_history,
+                    max_new_tokens=st.session_state.max_response_length,
+                )
+                st.markdown(response)
+
+

requirements.txt

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+smolagents
+pandas
+langchain 
+langchain-community 
+sentence-transformers 
+faiss-cpu
+langchain_huggingface
+langchain_core
+streamlit
+huggingface_hub
+transformers
+accelerate
+langchain_text_splitters
+beautifulsoup4
+playwright