Update app.py

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+import gradio as gr
 import os
 
 from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
@@ -14,12 +14,13 @@ from langchain.prompts.chat import (
 
 def create_db_from_video_url(video_url, api_key):
     """
-    Creates an Embedding of the Video and performs similarity search
+    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)
@@ -28,6 +29,9 @@ def create_db_from_video_url(video_url, api_key):
     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("GOOGLE_API_KEY")
     db = create_db_from_video_url(video, API_KEY)
     docs = db.similarity_search(query=request, k=5)
@@ -35,6 +39,7 @@ def get_response(video, request):
 
     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}
@@ -44,6 +49,8 @@ def get_response(video, request):
     """
 
     system_msg_prompt = SystemMessagePromptTemplate.from_template(template)
+
+    # human prompt
     human_template = "Answer the following questions: {question}"
     human_msg_prompt = HumanMessagePromptTemplate.from_template(human_template)
 
@@ -52,15 +59,25 @@ def get_response(video, request):
     )
 
     chain = LLMChain(llm=chat, prompt=chat_prompt)
+
     response = chain.run(question=request, docs=docs_content)
+
     return response
 
-# Streamlit interface
-st.title("YouTube Video AI Assistant")
-st.write("This application answers questions based on the specified YouTube video.")
+# 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
+)
 
-video_url = st.text_input("Enter the YouTube Video URL:", placeholder="Example: https://www.youtube.com/watch?v=MnDudvCyWpc")
-question = st.text_input("Enter your Question", placeholder="What's the video about?")
-if st.button("Get Answer"):
-    answer = get_response(video_url, question)
-    st.text_area("Answer:", value=answer, height=300)
+# launching the web app
+youtube_video_assistant.launch(share=True)