updated code

front.py

@@ -0,0 +1,165 @@
+import streamlit as st
+from llm_code import *
+
+# Frontend code
+st.set_page_config(page_title="🦙💬 Enchantty Chatbot")
+st.title("Enchantty")
+st.write("Upload your Hugging Face API token and chat with your PDF's and Websites", 
+         unsafe_allow_html=True, 
+         format="markdown", 
+         style={'font-size': '20px'})
+
+with st.sidebar:
+    st.markdown("*Getting a Hugging Face token*")
+    st.markdown("Steps:")
+    st.markdown("1. Navigate to [Hugging Face](https://huggingface.co/settings/tokens)")
+    st.markdown("2. Create a write token and copy it to your clipboard")
+    st.markdown("3. Paste the token in the input field below")
+    st.markdown("<hr/>", unsafe_allow_html=True)
+    st.markdown("*Hugging Face API Token*")
+    token_placeholder = st.empty()
+    HF_token = token_placeholder.text_input("Enter your Hugging Face API token", type="password")
+
+    # Check if HF_token is provided
+    if HF_token:
+        # Replace the token input field with the success message
+        token_placeholder.empty()
+        st.success('API key provided!', icon='✅')
+    else:
+        st.warning('Please enter your Hugging Face API token!', icon='⚠️')
+
+    # Changing from here 
+    if HF_token:
+
+        st.markdown("<h2 style='text-align:center;font-family:Georgia;font-size:20px;'>Choose Model</h2>",
+                    unsafe_allow_html=True)
+
+        # Add a dropdown menu to select LLM model
+        selected_model = st.selectbox("Select LLM Model", list(llm_models.keys()))
+
+        # Display warning for models with gated access
+        if selected_model == "Llama-2-7B (Gated Access)" or selected_model == "Gemma-7B (Gated Access)" or selected_model == "Gemma-7B-it (Gated Access)" :
+            st.warning("Access to this model requires authorization from Hugging Face.")
+
+        file_or_url_placeholder = st.empty()
+        file_or_url = st.radio("Choose Input Type", ("PDF File", "Website"))
+
+        if file_or_url == "PDF File":
+            uploaded_file = st.file_uploader('Upload your .pdf file', type="pdf")
+            if uploaded_file is not None:
+                # Replace the PDF upload input field with the success message
+                file_or_url_placeholder.empty()
+                st.success('PDF file uploaded successfully!', icon='✅')
+                # Save the uploaded file to a temporary location and process it
+                with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+                    tmp_file.write(uploaded_file.getvalue())
+                    content = PDFPlumberLoader(tmp_file.name).load()
+
+        elif file_or_url == "Website":
+            url_placeholder = st.empty()
+            url = st.text_input("Enter the URL")
+            if url.strip():
+                # Replace the URL input field with the success message
+                url_placeholder.empty()
+                st.success('URL entered successfully!', icon='✅')
+                # Process the URL
+                content = WebBaseLoader(url).load()
+
+        st.markdown("<h2 style='text-align:center;font-family:Georgia;font-size:20px;'>Advanced Features</h1>",
+                    unsafe_allow_html=True)
+        max_length = st.slider("Token Max Length", min_value=256, max_value=1024, value=256, step=128)
+        temp = st.slider("Temperature", min_value=0.1, max_value=1.0, value=0.1, step=0.1)
+
+
+if 'content' in locals():
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
+    chunking = text_splitter.split_documents(content)
+    embeddings = HuggingFaceInferenceAPIEmbeddings(
+        api_key=HF_token,
+        model_name="sentence-transformers/all-MiniLM-L6-v2"
+    )
+    vectorstore = FAISS.from_documents(chunking, embeddings)
+    prompt = hub.pull("rlm/rag-prompt", api_url="https://api.hub.langchain.com")
+
+    # Get the selected LLM model ID
+    selected_model_id = llm_models[selected_model]
+
+    def model(user_query, max_length, temp):
+        record_timing()  # Record time before generating response
+        llm = HuggingFaceHub(
+            repo_id=selected_model_id,
+            huggingfacehub_api_token=HF_token,
+            model_kwargs={"max_length": max_length, "temperature": temp}
+        )
+        retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 2})
+        qa = RetrievalQA.from_chain_type(llm=llm,
+                                        chain_type="stuff",
+                                        retriever=retriever,
+                                        return_source_documents=True,
+                                        verbose=True,
+                                        chain_type_kwargs={"prompt": prompt})
+        response = qa(user_query)["result"]
+
+        answer_start = response.find("Answer:")
+        if answer_start != -1:
+            answer = response[answer_start + len("Answer:"):].strip()
+            return answer
+        else:
+            return "Sorry, I couldn't find the answer."
+
+    # Reset chat when the model selection changes
+    if "selected_model" in st.session_state:
+        if st.session_state.selected_model != selected_model:
+            st.session_state.messages = []
+    st.session_state.selected_model = selected_model
+
+    # CSS styling for the text input
+    styl = f"""
+    <style>
+        .stTextInput {{
+            position: fixed;
+            bottom: 3rem;
+        }}
+    </style>
+    """
+    st.markdown(styl, unsafe_allow_html=True)
+
+    if "widget" not in st.session_state:
+        st.session_state.widget = ''
+    if "messages" not in st.session_state.keys():
+        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+    # Display or clear chat messages
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+    def clear_chat_history():
+        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+    st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+
+    def submit():
+        record_timing()  # Record time before submitting message
+        st.session_state.something = st.session_state.widget
+        st.session_state.widget = ''
+
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}]
+
+    if user_prompt := st.chat_input("enter your query"):
+        st.session_state.messages.append({"role": "user", "content": user_prompt})
+        with st.chat_message("user"):
+            st.write(user_prompt)
+
+    if st.session_state.messages[-1]["role"] != "assistant":
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response = model(user_prompt, max_length, temp)
+                placeholder = st.empty()
+                full_response = ''
+                for item in response:
+                    full_response += item
+                    placeholder.markdown(full_response)
+                placeholder.markdown(full_response)
+        message = {"role": "assistant", "content": full_response}
+        st.session_state.messages.append(message)

llm_code.py

@@ -0,0 +1,33 @@
+import os
+import time
+import tempfile
+import pdfplumber
+from langchain.chains import RetrievalQA
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceHub
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain_community.document_loaders import PDFPlumberLoader, WebBaseLoader
+from langchain import hub
+import re
+
+# Function to record time
+time_start = 0
+def record_timing():
+    global time_start
+    if time_start != 0:
+        duration = time.time() - time_start
+        print(f"Time taken for query-response pair: {duration:.2f} seconds")
+    time_start = time.time()
+
+# List of available LLM models
+llm_models = {
+    "Mistral-7B-Instruct-v0.2": "mistralai/Mistral-7B-Instruct-v0.2",
+    "Mixtral-8x7B-Instruct-v0.1": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "Gemma-7B (Gated Access)": "google/gemma-7b",
+    "Gemma-7B-it (Gated Access)":"google/gemma-7b-it",
+    "Zephyr":"HuggingFaceH4/zephyr-7b-beta",
+    "Google-2b (Gated Access)":"google/gemma-2b-it"
+}
+