Create app.py

app.py

@@ -0,0 +1,82 @@
+import torch, os, argparse, shutil, textwrap, time, streamlit as st 
+from langchain.document_loaders import YoutubeLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings, HuggingFaceBgeEmebddings
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI 
+from langchain.chat_models import ChatOpenAI
+from langchain import HuggingFaceHub
+from transformers import pipeline 
+from deep_translator import GoogleTranslator
+from langdetect import detect 
+
+def typewriter(text, speed):
+    container = st.empty()
+    displayed_text = ''
+
+    for char in text:
+        displayed_text += char
+        container.markdown(displayed_text)
+        time.sleep(1 / speed)
+
+def wrap_text_preserve_newlines(text, width=110):
+    lines = text.split('\n')
+    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
+    wrapped_text = '\n'.join(wrapped_lines)
+    return wrapped_text
+
+def process_llm_response(llm_originalresponse2):
+    typewriter(llm_originalresponse2['result'], speed=40)
+
+def extract_video_id(youtube_url):
+    try:
+        parsed_url = urlparse(youtube_url)
+        query_params = parse_qs(parsed_url.query)
+        video_id = query_params.get('v', [None])[0]
+
+        return video_id
+    except Exception as e:
+        print(f"Error extracting video ID: {e}")
+        return None 
+
+def chat():
+    HF_TOKEN = os.environ.get('HF_TOKEN', False)
+    model_name = "BAAI/bge-base-en"
+    encode_kwargs = {'normalize_embeddings': True}
+
+    st.title('YouTube ChatBot')
+
+    video_url = st.text_input('Insert video URL', placeholder='Format should be like: https://www.youtube.com/watch?v=pSLeYvld8Mk')
+    query = st.text_input("Ask any question about the video")
+
+    if st.button('Submit', type='primary'):
+        with st.spinner('Processing the video...'):
+            video_id = extract_video_id(video_url)
+            loader = YoutubeLoader(video_id)
+            documents = loader.load()
+
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_ovelap=100)
+            documents = text_splitter.split_documents(documents)
+
+            vector_db = Chroma.from_documents(
+                documents,
+                embeddings = HuggingFaceBgeEmebddings(model_name=model_name, model_kwargs={'device'L 'cuda' if torch.cuda.is_available() else 'cpu'}, encode_kwargs=encode_kwargs)
+            )
+            repo_id = "tiiuae/falcon-7b-instruct"
+            qa_chain = RetrievalQA.from_chain_type(
+                llm=HuggingFaceHub(
+                    huggingfacehub_api_token=HF_TOKEN, 
+                    repo_id=repo_id, 
+                    model_kwargs={'temperature': 0.1, 'max_new_tokens': 1000},
+                )
+                retriever=vector_db.as_retriever(),
+                return_source_documents=False,
+                verbose=False
+            )
+
+        with st.spinner('Generating Answer...'):
+            llm_response = qa_chain(query)
+            process_llm_response(llm_response)
+
+chat()