Create app.py

app.py

@@ -0,0 +1,140 @@
+import streamlit as st
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.llms import LlamaCpp
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download 
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from huggingface_hub import hf_hub_download
+from langchain.chains.summarize import load_summarize_chain
+from langchain.document_loaders import YoutubeLoader
+import streamlit as st
+from langchain.chains import RetrievalQA
+from langchain import PromptTemplate
+import os
+import re
+from langchain_community.llms import Ollama
+
+template = f"""Use the following pieces of information to answer the user's question.
+If you don't know the answer or if the data might be outdated, just say that you don't know or acknowledge the potential time lapse, don't try to make up an answer.
+
+Context: {{context}}
+Question: {{question}}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+def set_custom_prompt():     
+    prompt = PromptTemplate(template=template, input_variables=['context', 'question'])
+    return prompt
+
+def load_data(url):
+  data1 = YoutubeLoader.from_youtube_url(url , add_video_info=True)
+  data = data1.load()
+  return data
+
+def split_data(data):
+  splitter = RecursiveCharacterTextSplitter(chunk_size = 2000 , chunk_overlap = 200)
+  splits = splitter.split_documents(data)
+  return splits
+
+def init_llm():
+  model_name = "google/gemma-2b-it"
+  model_file = "gemma-2b-it.gguf"
+  HF_TOKEN = st.secrets["HF_TOKEN"]
+  model_pth = hf_hub_download(model_name,
+                                  filename=model_file,
+                                  token= HF_TOKEN)
+  callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])  
+  llm = LlamaCpp(model_path = model_pth ,  max_tokens = 2000  , n_gpu_layers = -1 ,callback_manager= callback_manager, verbose=True,)
+  # llm = Ollama(model="mistral:latest")
+  # return llm 
+  
+def init_db(splits):
+  embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                       model_kwargs={'device': 'cpu'})
+  Chroma.from_documents(splits , embedding_func ,  persist_directory="./chroma_db5")
+
+def init_chain(llm , db_chroma , prompt): 
+    qa_chain = RetrievalQA.from_chain_type(llm=llm,
+                                            chain_type='stuff',
+                                            retriever= db_chroma.as_retriever(search_kwargs={ "k": 4}),
+                                            chain_type_kwargs={'prompt': prompt},
+                                            )
+    return qa_chain
+
+def chat_bot():
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                       model_kwargs={'device': 'cpu'})
+    db_chroma = Chroma(persist_directory="./chroma_db5", embedding_function= embeddings)
+    llm = init_llm()
+    qa_prompt = set_custom_prompt()
+    qa = init_chain(llm,  db_chroma  , qa_prompt)
+    return qa
+  
+st.set_page_config(page_title="InsightBOT : Your YouTube Companion" ,page_icon = "🤖")  
+st.title("InsightBOT 🤖")
+
+st.sidebar.subheader("Youtube URL 🔗")
+
+url = st.sidebar.text_input('Enter Youtube Video URL:')
+
+if st.sidebar.button("Analyze Video"):
+    st.video(url, format='video/mp4')
+    
+    with st.spinner("Extracting insights... 🧠💡"):
+        data = load_data(url)
+        splits = split_data(data)
+        db = init_db(splits)
+   
+if st.sidebar.button('Summarise Video'):
+    with st.spinner('Writing video synopsis... 🖊️'):
+        data = load_data(url)
+        splits = split_data(data)
+        llm = init_llm()
+        sum_chain = load_summarize_chain(llm  = llm , chain_type = "map_reduce")
+        summary = sum_chain.run(splits)
+        st.write(summary)
+
+st.markdown("Summarise and Engage with Your Video Content! 💡")
+
+st.sidebar.markdown("---")
+st.sidebar.caption("Created By: Sidhant Manale ")
+               
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": f"Hey! How can I help you?"}]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+st.sidebar.markdown("#")
+st.sidebar.markdown("#")
+st.sidebar.subheader("Clear Chat")
+if st.sidebar.button("Reset"):
+    st.session_state.messages = []
+
+if user_prompt := st.chat_input():
+    
+        st.session_state.messages.append({"role": "user", "content": user_prompt})
+        st.chat_message("user").write(user_prompt)
+        with st.spinner("Thinking..."):
+            qa_result = chat_bot()
+            response = qa_result({'query': user_prompt})
+            bot_answer = response['result']
+              
+            if 'source_documents' in response and response['source_documentsz ']:
+                document = response['source_documents'][0]
+                metadata = document.metadata
+                file = metadata['source'].split("\\")[-1]
+                source = os.path.splitext(file)[0]
+                assistant_answer = f"{bot_answer} \n\n Source : {source} Video"
+            else:
+                source = "Llama"
+                assistant_answer = f"{bot_answer} \n\n Source : {source} Model"
+                
+            st.session_state.messages.append({"role": "assistant", "content": bot_answer})
+            st.chat_message("assistant").write(bot_answer)
+