html templates has been added

app.py

@@ -1,20 +1,109 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from huggingface_hub import login
+import os
+import time
+import streamlit as st
+from dotenv import load_dotenv
+from htmlTemplates import css, bot_template, user_template
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFaceHub
+from langchain import PromptTemplate
+from pdfminer.high_level import extract_text
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 
-import streamlit as st
+# Updated Prompt Template
+template = """You are an expert on TeamCenter. Use the following pieces of context to answer the question at the end. 
+If you don't know the answer, it's okay to say that you don't know. Please don't try to make up an answer. 
+Use two sentences minimum and keep the answer as concise as possible (maximum 200 characters each).
+Always use proper grammar and punctuation. End of the answer always say "End of answer" (without quotes).
+
+Context:
+{context}
+
+Question: {question}
+Helpful Answer (Two sentences minimum, maximum 200 characters each):"""
+
+tokenizer = AutoTokenizer.from_pretrained("red1xe/falcon-7b-codeGPT-3K")
+model = AutoModelForSeq2SeqLM.from_pretrained("red1xe/falcon-7b-codeGPT-3K")
+## QA_CHAIN_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
+
+load_dotenv()
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
+model_path = os.environ.get('MODEL_PATH')
+
+def get_vector_store(target_source_chunks):
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    retriver = db.as_retriever(search_kwargs={"k": target_source_chunks})
+    return retriver
+
+def get_conversation_chain(retriever):
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True,)
+    chain = RetrievalQA.from_llm(
+        llm=model,
+        memory=memory,
+        retriever=retriever,
+    )
+    return chain
+
+
+def handle_userinput(user_question):
+    if st.session_state.conversation is None:
+        st.warning("Please load the Vectorstore first!")
+        return
+    else:
+        with st.spinner('Thinking...', ):
+            start_time = time.time()
+            response = st.session_state.conversation({'query': user_question})
+            end_time = time.time()
+
+            st.session_state.chat_history = response['chat_history']
+
+            for i, message in enumerate(st.session_state.chat_history):
+                if i % 2 == 0:
+                    st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+                else:
+                    st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+
+            st.write('Elapsed time: {:.2f} seconds'.format(end_time - start_time))
+            st.balloons()
+
+
+
+
+def main():
+
+    st.set_page_config(page_title='Chat with PDF', page_icon=':rocket:', layout='wide', )
+    with st.sidebar.title(':gear: Parameters'):
+        model_n_ctx = st.sidebar.slider('Model N_CTX', min_value=128, max_value=2048, value=1024, step=2)
+        model_n_batch = st.sidebar.slider('Model N_BATCH', min_value=1, max_value=model_n_ctx, value=512, step=2)
+        target_source_chunks = st.sidebar.slider('Target Source Chunks', min_value=1, max_value=10, value=4, step=1)
+    st.write(css, unsafe_allow_html=True)
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+
+    st.header('Chat with PDF :robot_face:')
+    st.subheader('Upload your PDF file and start chatting with it!')
+    user_question = st.text_input('Enter your message here:')
+
+    if st.button('Start Chain'):
+        with st.spinner('Working in progress ...'):
+            vector_store = get_vector_store(target_source_chunks)
+            st.session_state.conversation = get_conversation_chain(
+                retriever=vector_store,
+            )
 
-st.set_page_config(
-    page_title="Code Generation",
-    page_icon="🤖",
-    layout="wide",
-    initial_sidebar_state="expanded",
-)
-login(token='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', add_to_git_credential=True)
+    if user_question:
+        handle_userinput(user_question)
 
-st.title("Code Generation")
-st.write('MODEL: TinyPixel/red1xe/Llama-2-7B-codeGPT')
-model_name='lmsys/vicuna-7b-v1.1'
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model= AutoModelForCausalLM.from_pretrained(model_name)
 
+if __name__ == '__main__':
+    main()

htmlTemplates.py

@@ -0,0 +1,44 @@
+css = '''
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 70%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+'''
+
+bot_template = '''
+<div class="chat-message bot">
+    <div class="avatar">
+        <img src="https://media.licdn.com/dms/image/C4E0BAQHSBVuHaXe6DA/company-logo_200_200/0/1519893058477?e=1697673600&v=beta&t=ISf-4u1cOnN0KNQSNLt5MY5RvxYL50PIFIyvuUrIf9g" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+user_template = '''
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://cdn-icons-png.flaticon.com/512/1077/1077012.png">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''