Create app.py

app.py

@@ -0,0 +1,84 @@
+import gradio as gr
+import os
+
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from langchain.document_loaders import YoutubeLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chains import LLMChain
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+def create_db_from_video_url(video_url, api_key):
+    """
+    Creates an Embedding of the Video and performs 
+    """
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
+
+    loader = YoutubeLoader.from_youtube_url(video_url)
+    transcripts = loader.load()
+    # cannot provide this directly to the model so we are splitting the transcripts into small chunks
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    docs = text_splitter.split_documents(transcripts)
+
+    db = FAISS.from_documents(docs, embedding=embeddings)
+    return db
+
+def get_response(video, request):
+    """
+    Usind Gemini Pro to get the response. It can handle upto 32k tokens.
+    """
+    API_KEY = os.environ.get("API_Key")
+    db = create_db_from_video_url(video, API_KEY)
+    docs = db.similarity_search(query=request, k=5)
+    docs_content = " ".join([doc.page_content for doc in docs])
+
+    chat = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=API_KEY, convert_system_message_to_human=True)
+
+    # creating a template for request
+    template = """
+    You are an assistant that can answer questions about youtube videos based on
+    video transcripts: {docs}
+    Only use factual information from the transcript to answer the question.
+    If you don't have enough information to answer the question, say "I don't know".
+    Your Answers should be detailed.
+    """
+
+    system_msg_prompt = SystemMessagePromptTemplate.from_template(template)
+
+    # human prompt
+    human_template = "Answer the following questions: {question}"
+    human_msg_prompt = HumanMessagePromptTemplate.from_template(human_template)
+
+    chat_prompt = ChatPromptTemplate.from_messages(
+        [system_msg_prompt, human_msg_prompt]
+    )
+
+    chain = LLMChain(llm=chat, prompt=chat_prompt)
+
+    response = chain.run(question=request, docs=docs_content)
+
+    return response
+
+# creating title, description for the web app
+title = "YouTube🔴 Video🤳  AI Assistant 🤖"
+description = "Answers to the Questions asked by the user on the specified YouTube video."
+
+
+# building the app
+youtube_video_assistant = gr.Interface(
+    fn=get_response,
+    inputs=[gr.Text(label="Enter the Youtube Video URL:", placeholder="Example: https://www.youtube.com/watch?v=MnDudvCyWpc"),
+            gr.Text(label="Enter your Question", placeholder="Example: What's the video is about?")],
+    outputs=gr.TextArea(label="Answers using....some secret llm 🤫😉:"),
+    title=title,
+    description=description,
+    article=article
+)
+
+# launching the web app
+youtube_video_assistant.launch()