Create app.py

app.py

@@ -0,0 +1,112 @@
+from langchain_community.llms import CTransformers
+import gradio as gr
+import os
+from langchain.prompts import PromptTemplate
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_community.vectorstores import Chroma
+from fpdf import FPDF
+from youtube_transcript_api import YouTubeTranscriptApi
+from langchain.chains import RetrievalQA
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+import random
+
+def text_to_pdf(url, filename="output.pdf"):
+    video_id = url.split('=')[1]
+    transcript = YouTubeTranscriptApi.get_transcript(video_id)
+    res = ""
+    for i in transcript:
+        res += " " + i["text"]
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.add_font('DejaVu', '', 'DejaVuSansCondensed.ttf', uni=True)
+    pdf.set_font("DejaVu",'', size=12)
+    pdf.multi_cell(0, 10, res)
+    pdf.output(filename)
+    print(f"{filename} generated!")
+
+local_llm = "llama-2-13b-chat.Q4_K_M.gguf"
+
+config = {
+    'max_new_tokens':512,
+    'context_length':700,
+    'repetition_penalty':1.5,
+    'temperature':0.4,
+    'top_k':50,
+    'top_p':0.9,
+    'stream':True,
+    'threads':int(os.cpu_count()/2)
+}
+
+llm_init = CTransformers(
+    model=local_llm,
+    model_type="llama",
+    lib="avx2",
+    **config
+)
+
+prompt_template = """Use the following pieces of information to answer the user's question.
+Make sure to adhere by the user's request.
+
+Context: {context}
+Question: {question}
+
+Relevant Answer:
+"""
+
+model_name = "BAAI/bge-large-en"
+model_kwargs = {'device': 'cpu'}
+encode_kwargs = {'normalize_embeddings': False}
+embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+def process_transcript():
+    loader = PyPDFLoader("output.pdf")
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    texts = text_splitter.split_documents(documents)
+    # print(texts)
+    num = random.randint(0, 10000)
+    vectorstore = Chroma.from_documents(texts, embeddings, collection_metadata={"hnsw:space":"cosine"}, persist_directory=f"stores/tcp_cosine_{num}")
+    print("Vector Store created!")
+    return vectorstore
+
+def get_response(url):
+    text_to_pdf(url)
+    load_vector_store = process_transcript()
+    prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
+    retriever = load_vector_store.as_retriever(search_kwargs={"k":1})
+    chain_type_kwargs = {"prompt": prompt}
+    query = "Summarise the given context in third person perspective in format of bullet points. Make sure to cover the entire content and only provide the crucial important gist in your response. Be as descriptive as you want, but keep the content relevant."
+    qa = RetrievalQA.from_chain_type(
+        llm=llm_init,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=False,
+        chain_type_kwargs=chain_type_kwargs,
+        verbose=True
+    )
+    response = qa(query)
+    return response['result']
+
+input = gr.Text(
+    label="Prompt",
+    show_label=False,
+    max_lines=1,
+    placeholder="Enter your URL",
+    container=False
+)
+
+iface = gr.Interface(
+    fn=get_response,
+    inputs=input,
+    outputs="text",
+    title="YouTube Video Summarizer",
+    description="Enter the URL to the YouTube video that you want to summarize: ",
+    allow_flagging=False
+)
+
+iface.launch()