Upload app.py

app.py

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+import streamlit as st
+hidden_style = """
+            <style>
+            #MainMenu  {visibility: hidden;}
+            footer  {visibility: hidden;}
+            </style>
+            """
+st.markdown(hidden_style, unsafe_allow_html=True)
+
+def basic_version():
+    import argparse
+    import os
+    import shutil
+    import time
+    import torch
+    import textwrap
+    from urllib.parse import urlparse, parse_qs
+    from dotenv import load_dotenv
+    from langdetect import detect
+    from deep_translator import GoogleTranslator
+    from transformers import pipeline
+    import streamlit as st
+    from langchain import HuggingFaceHub
+    from langchain.chains import RetrievalQA
+    from langchain.chat_models import ChatOpenAI
+    from langchain.document_loaders import YoutubeLoader
+    from langchain.embeddings import HuggingFaceBgeEmbeddings
+    from langchain.embeddings import OpenAIEmbeddings
+    from langchain.embeddings import HuggingFaceInstructEmbeddings
+    from langchain.llms import OpenAI
+    from langchain.text_splitter import RecursiveCharacterTextSplitter
+    from langchain.vectorstores import Chroma
+    load_dotenv()
+
+    def text_writer(input_text: str, speed: float):
+        container = st.empty()
+        displayed_text = ""
+
+        for char in input_text:
+            displayed_text += char
+            container.markdown(displayed_text)
+            time.sleep(1/speed)
+
+    def wrap_text_keep_newlines(input_text, width=110):
+        lines = input_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_response(original_response):
+        text_writer(original_response["result"], speed=40)
+
+    def get_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 start_basic_version():
+        HUGGINGFACE_API_TOKEN = os.environ["HUGGINGFACE_API_TOKEN"]
+        model_name = "BAAI/bge-base-en"
+        encode_kwargs = {'normalize_embeddings': True}
+
+        st.title('Chat with Youtube 🎬🤖')
+        st.markdown(""" Using AI to interact with Youtube! """)
+
+        video_url = st.text_input("Insert The video URL",  placeholder="Format should be like: https://www.youtube.com/watch?v=pSLeYvld8Mk")
+        query = st.text_input("Ask any question about the video",help="Suggested queries: Summarize the key points of this video - What is this video about - Ask about a specific thing in the video ")
+        st.warning("⚠️ Please Keep in mind that the accuracy of the response relies on the :red[Video's quality] and the :red[prompt's Quality]. Occasionally, the response may not be entirely accurate. Consider using the response as a reference rather than a definitive answer.")
+
+        if st.button("Submit Question", type="primary"):
+            with st.spinner('Processing the Video...'):
+                video_id = get_video_id(video_url)
+                loader = YoutubeLoader(video_id)
+                documents = loader.load()
+
+                text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+                documents = text_splitter.split_documents(documents)
+                if os.path.exists('./data'):
+                    shutil.rmtree('./data')
+                vector_db = Chroma.from_documents(
+                    documents,
+                    embedding= HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs={'device': '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=HUGGINGFACE_API_TOKEN,
+                                    repo_id=repo_id,
+                                    model_kwargs={"temperature":0.2, "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_response(llm_response)
+    start_basic_version()
+
+basic_version()
+
+st.sidebar.markdown("## Chat with Youtube using AI 🎬🤖") 
+st.sidebar.markdown("""Built by <a href="https://github.com/Ahmet-Dedeler"> Ahmet </a> & <a href="https://github.com/arhaamwanii"> Arhaam </a> for MLH All in Open Source Hackathon.""", unsafe_allow_html=True)
+st.sidebar.markdown('<a href="https://github.com/Ahmet-Dedeler/Chat-With-Youtube_All-In-Hackathon"> Check out the project on GitHub <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/github/github-original.svg" alt="GitHub" width="30" height="30"></a>', unsafe_allow_html=True)