add .env

.env

@@ -0,0 +1 @@
+OPENAI_API_KEY="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"

main.py

@@ -1,78 +0,0 @@
-
-from langchain.document_loaders import YoutubeLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-from langchain.vectorstores import FAISS
-from dotenv import load_dotenv
-import gradio as gr
-from langchain.document_loaders import YoutubeLoader
-
-
-load_dotenv()
-
-embeddings = OpenAIEmbeddings()
-
-# video_url = "https://www.youtube.com/watch?v=PfTOr3ONKzU"
-def create_vector_db_from_youtube_url(video_url: str):
-    loader = YoutubeLoader.from_youtube_url(video_url)
-    transcript = loader.load()
-    
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-    docs = text_splitter.split_documents(transcript)
-    
-    db = FAISS.from_documents(docs, embeddings)
-    return db
-
-
-
-
-# create_vector_db_from_youtube_url(video_url)
-
-def get_response_from_query(db, query, k=4):
-  docs = db.similarity_search(query, k=k)
-  docs_page_content = " ".join([d.page_content for d in docs])
-
-  llm = OpenAI(model_name="text-davinci-003")
-  prompt = PromptTemplate(
-      input_variables=["question", "docs"],
-      template = """
-      Youare a helpful Youtube assistant that can answer questions about videos based on video transcript.
-      
-      Answer the following question: {question}
-      By searching the following video transcript: {docs}
-
-      Only use the factua; information from the transcript to answer the question.
-
-      If you feel like you dont have enough information to answer the question, say "I dont know".
-
-      Your answer ahould be detailed.
-      """
-  )
-
-  chain = LLMChain(llm=llm, prompt=prompt)
-
-  response = chain.run(question = query, docs = docs_page_content)
-  response = response.replace("\n", " ")
-  return response
-
-
-def gradio_interface(youtube_url, query):
-    if query and youtube_url:
-        db = create_vector_db_from_youtube_url(youtube_url)
-        response = get_response_from_query(db, query)
-        return response
-
-# Membuat antarmuka Gradio
-iface = gr.Interface(
-    fn=gradio_interface,
-    inputs=["text", "text"],  # Dua input teks: URL YouTube dan pertanyaan
-    outputs="text",          # Output berupa teks
-    title="YouTube Assistant",
-    description="Masukkan URL YouTube dan ajukan pertanyaan tentang video tersebut."
-)
-
-# Menjalankan antarmuka Gradio
-iface.launch()