Create app.py

app.py

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+import gradio as gr
+import streamlit as st
+from llama_index import VectorStoreIndex, ServiceContext, Document
+from llama_index.llms import OpenAI
+import openai
+from llama_hub.youtube_transcript import YoutubeTranscriptReader
+
+from llama_index import VectorStoreIndex
+
+from langchain.embeddings import HuggingFaceEmbeddings
+from llama_index import LangchainEmbedding, ServiceContext
+from llama_index.llm_predictor import LLMPredictor
+from langchain.llms import LlamaCpp
+
+
+## For embedding the video, we will use the Hugging Face Sentence Transformers
+model_name = "sentence-transformers/all-mpnet-base-v2"
+hf = HuggingFaceEmbeddings(
+    model_name=model_name
+)
+
+### We are using LlamaCPP to load the LLAMA-2-18 8 bit quantised model in GGUF format 
+llm = LlamaCpp(
+   
+    model_path="codeup-llama-2-13b-chat-hf.Q8_0.gguf",
+    n_gpu_layers=-1,
+    n_batch=512,
+    temperature=0.1,
+    max_tokens=256,
+    top_p=1,
+    verbose=True, 
+    f16_kv=True,
+    n_ctx=4096,
+    use_mlock=True,n_threads=4,
+    stop=["Human:","User:"]
+
+)
+
+## Create a service context object, that will allow us to use the Hugging Face embeddings and llama 2 model as our Language model
+llm_predictor=LLMPredictor(llm=llm)
+embed_model = LangchainEmbedding(hf) 
+service_context = ServiceContext.from_defaults(embed_model=embed_model,llm_predictor=llm_predictor)
+index=None
+
+
+
+'''
+The load data function , takes in youtube_url and allows us to index the youtube video.
+'''
+
+def load_data(youtube_url):
+    print("In Load Data")
+
+    if youtube_url.strip()=="":
+        st.error("Enter A youtube URL")
+        return None
+    else:
+        try:
+            loader = YoutubeTranscriptReader()
+            documents = loader.load_data(ytlinks=[youtube_url])
+    
+        
+            index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+            return index
+        except:
+            print("Enter a valid youtube URL")
+            st.error("Enter a valid youtube URL")
+            return None
+
+#### We will have user enter the youtube_url and press submit => which loads the index
+index=None
+
+
+chat_engine=None
+
+### we initiate twp session_state object : clicked and index.
+### Clicked: This is set to true when the Submit button is clicked.
+### Index: This stores the vector index. By keeping this session state, we allow the index to be persistent till a new yoputube url is enteres
+
+if 'clicked' not in st.session_state:
+    st.session_state.clicked = False
+if 'index' not in st.session_state:
+    st.session_state.index=None
+
+### click_button-> changes state to Truw when button is clicked 
+def click_button():
+    st.session_state.clicked = True
+with st.sidebar:
+    st.title("Youtube QA with Llama 2 Bot")
+             
+    st.subheader("Upload Documents/URL")
+    youtube_url = st.sidebar.text_input('Enter Youtube URL', '')
+    submit_btn=st.sidebar.button('Submit',on_click=click_button)
+    ## When the submit button is clicked, load the data and set the index session_state to the loaded index
+    if st.session_state.clicked: 
+        print("Going to Load Data")
+        index=load_data(youtube_url)
+        st.session_state.index=index
+        print("Index ",index)
+        
+        st.session_state.clicked=False # set it to false , so that load_data function is not called for every single user message
+
+
+
+#print("Index",index)
+
+print("Index State ",st.session_state.index)
+### If the index has been loaded, create the chat_engine object
+if st.session_state.index!=None:
+    chat_engine=st.session_state.index.as_chat_engine(verbose=True,chat_mode="context",service_context=service_context)
+    print("CHat engine",chat_engine) 
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+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)
+
+if prompt := st.chat_input():
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
+    full_response = ''
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            print("Calling CHat Engine")
+            if chat_engine!=None:
+                response = chat_engine.stream_chat(prompt)
+                placeholder = st.empty()
+                
+                for item in response.response_gen:
+                    full_response += item
+                    placeholder.markdown(full_response.strip("Assistant:"))
+                placeholder.markdown(full_response)
+    if full_response!="":
+        message = {"role": "assistant", "content": full_response}
+        st.session_state.messages.append(message)
+