Update app.py

app.py

@@ -1,179 +1,71 @@
-
-import streamlit as st
-hide_streamlit_style = """
-            <style>
-            #MainMenu  {visibility: hidden;}
-            footer  {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-def free_version():
-          import torch
-          import os
-          import argparse
-          import shutil
-          from langchain.document_loaders import YoutubeLoader
-          from langchain.text_splitter import RecursiveCharacterTextSplitter
-          from langchain.vectorstores import Chroma
-          from langchain.embeddings import OpenAIEmbeddings
-          from langchain.chains import RetrievalQA
-          from langchain.llms import OpenAI
-          import streamlit as st
-          from langchain.chat_models import ChatOpenAI
-          from langchain import HuggingFaceHub
-          from langchain.embeddings import HuggingFaceInstructEmbeddings
-          from urllib.parse import urlparse, parse_qs
-          from langchain.embeddings import HuggingFaceBgeEmbeddings
-          from transformers import pipeline
-          import textwrap
-          import time
-          from deep_translator import GoogleTranslator
-          from langdetect import detect
-
-
-          def typewriter(text: str, speed: float):
-                      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):
-                      # Split the input text into lines based on newline characters
-                      lines = text.split('\n')
-
-                      # Wrap each line individually
-                      wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
-
-                      # Join the wrapped lines back together using newline characters
-                      wrapped_text = '\n'.join(wrapped_lines)
-                      return wrapped_text
-          def process_llm_response(llm_originalresponse2):
-                      #result_text = wrap_text_preserve_newlines(llm_originalresponse2["result"])
-                      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 set_openAi_api_key(api_key: str):
-                      st.session_state["OPENAI_API_KEY"] = api_key
-                      os.environ['OPENAI_API_KEY'] = api_key
-          def openai_api_insert_component():
-                      with st.sidebar:
-                          st.markdown(
-                              """
-                              ## Quick Guide 🚀
-                              1. Get started by adding your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑
-                              2. Easily input the video url
-                              3. Engage with the content - ask questions, seek answers💬
-                              """
-                          )
-
-                          api_key_input = st.text_input("Input your OpenAI API Key",
-                                                      type="password",
-                                                      placeholder="Format: sk-...",
-                                                      help="You can get your API key from https://platform.openai.com/account/api-keys.")
-
-
-                          if api_key_input == "" or api_key_input is None:
-                                  st.sidebar.caption("👆 :red[Please set your OpenAI API Key here]")
-
-
-                          st.caption(":green[Your API is not stored anywhere. It is only used to generate answers to your questions.]")
-
-                          set_openAi_api_key(api_key_input)
-
-          def launchfreeversion():
-                  HUGGINGFACE_API_TOKEN = os.environ['access_code']
-                  model_name = "BAAI/bge-base-en"
-                  encode_kwargs = {'normalize_embeddings': True}
-
-                  st.title('MKG: Your Chat with Youtube Assistant')
-
-                  videourl = 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 = extract_video_id(videourl)
-                        loader = YoutubeLoader(video_id)
-                        documents = loader.load()
-
-                        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-                        documents = text_splitter.split_documents(documents)
-                        
-                        
-                        vectordb = Chroma.from_documents(
-                        documents,
-                        #embedding = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
-                                                                                              # model_kwargs={"device": "cuda"})
-                        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":0.1, "max_new_tokens":1000}),
-                            retriever=vectordb.as_retriever(),
-                            return_source_documents=False,
-                            verbose=False
-                        )
-                    with st.spinner('Generating Answer...'):
-                          llm_response = qa_chain(query)
-                          #llm_originalresponse2=llm_response['result']
-                          process_llm_response(llm_response)
-          launchfreeversion()
-
-
-def intro():
-            st.markdown("""
-            # MKG: Your Chat with Youtube Assistant 🎬🤖
-
-            Welcome to MKG-Assistant, where AI meets Youtube! 🚀🔍
-
-            ## Base Models
-
-            Q&A-Assistant is built on OpenAI's GPT 3.5 for the premium version and Falcon 7B instruct Model for the free version to enhance your websites browsing experience. Whether you're a student, researcher, or professional, we're here to simplify your interactions with the web. 💡📚
-
-            ## How to Get Started
-
-            1.Enter the Video URL.
-            2. Enter your API key.(Only if you chose the premium version. Key is not needed in the free version)
-            3. Ask questions using everyday language.
-            4. Get detailed, AI-generated answers.
-
-            5. Enjoy a smarter way to Interact with Youtube!
-
-
-
-            ## It is Time to Dive in!
-
-
-            """)
-page_names_to_funcs = {
-    "Main Page": intro,
-    "Open Source Edition (Free version)": free_version
-}
-
-
-
-
-
-
-#test
-demo_name = st.sidebar.selectbox("Choose a version", page_names_to_funcs.keys())
-page_names_to_funcs[demo_name]()
-st.sidebar.markdown('<a href="https://www.linkedin.com/in/mohammed-khalil-ghali-11305119b/"> Connect on LinkedIn <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/linkedin/linkedin-original.svg" alt="LinkedIn" width="30" height="30"></a>', unsafe_allow_html=True)
-st.sidebar.markdown('<a href="https://github.com/khalil-ghali"> Check out my 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)
+import gradio as gr
+import torch
+from langchain.document_loaders import YoutubeLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.chains import RetrievalQA
+from langchain import HuggingFaceHub
+from urllib.parse import urlparse, parse_qs
+
+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:
+        return f"Error extracting video ID: {e}"
+
+def process_video(youtube_url, question):
+    video_id = extract_video_id(youtube_url)
+    if not video_id:
+        return 'Invalid YouTube URL'
+
+    try:
+        # Initialize the YouTube Loader
+        loader = YoutubeLoader(video_id)
+        documents = loader.load()
+
+        # Process the documents
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+        documents = text_splitter.split_documents(documents)
+
+        # Initialize Vector Store
+        model_name = "BAAI/bge-base-en"
+        encode_kwargs = {'normalize_embeddings': True}
+        vectordb = 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)
+        )
+
+        # Setup the QA Chain
+        HUGGINGFACE_API_TOKEN = os.environ['HUGGINGFACE_API_TOKEN']
+        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":0.1, "max_new_tokens":1000}),
+            retriever=vectordb.as_retriever(),
+            return_source_documents=False,
+            verbose=False
+        )
+
+        # Process the question
+        llm_response = qa_chain(question)
+        return llm_response['result']
+    except Exception as e:
+        return f"Error processing video: {e}"
+
+iface = gr.Interface(
+    fn=process_video,
+    inputs=["text", "text"],
+    outputs="text",
+    title="YouTube Video AI Assistant",
+    description="Enter a YouTube URL and a question to get AI-generated answers based on the video."
+)
+
+if __name__ == "__main__":
+    iface.launch()