Delete app.py

app.py

@@ -1,159 +0,0 @@
-import gradio as gr
-from qdrant_client import models, QdrantClient
-from sentence_transformers import SentenceTransformer
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-# from langchain.llms import LlamaCpp
-from langchain.vectorstores import Qdrant
-from qdrant_client.http import models
-# from langchain.llms import CTransformers
-from ctransformers import AutoModelForCausalLM
-
-
-# loading the embedding model - 
-
-encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
-
-print("embedding model loaded.............................")
-print("####################################################")
-
-# loading the LLM 
-
-callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
-
-print("loading the LLM......................................")
-
-# llm = LlamaCpp(
-#     model_path="TheBloke/Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q8_0.gguf",
-#     n_ctx=2048,
-#     f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
-#     callback_manager=callback_manager,
-#     verbose=True,
-# )
-
-llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGUF", 
-                                           model_file="llama-2-7b-chat.Q3_K_L.gguf", 
-                                           model_type="llama", 
-                                           temperature = 0.2,
-                                           repetition_penalty = 1.5
-                                           )
-
-
-
-print("LLM loaded........................................")
-print("################################################################")
-
-def get_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(
-        # seperator = "\n",
-        chunk_size = 250,
-        chunk_overlap = 50,
-        length_function = len,
-    )
-
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-pdf_path = './100 Weird Facts About the Human Body.pdf'
-
-
-reader = PdfReader(pdf_path)
-text = ""
-num_of_pages = len(reader.pages)
-
-for page in range(num_of_pages):
-    current_page = reader.pages[page]
-    text += current_page.extract_text()
-
-
-chunks = get_chunks(text)
-print(chunks)
-print("Chunks are ready.....................................")
-print("######################################################")
-
-client = QdrantClient(path = "./db")
-print("db  created................................................")
-print("#####################################################################")
-
-client.recreate_collection(
-    collection_name="my_facts",
-    vectors_config=models.VectorParams(
-        size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
-        distance=models.Distance.COSINE,
-    ),
-)
-
-print("Collection created........................................")
-print("#########################################################")
-
-
-
-li = []
-for i in range(len(chunks)):
-    li.append(i)
- 
-dic = zip(li, chunks)
-dic= dict(dic)
-
-client.upload_records(
-    collection_name="my_facts",
-    records=[
-        models.Record(
-            id=idx,
-            vector=encoder.encode(dic[idx]).tolist(),
-            payload= {dic[idx][:5] : dic[idx]}
-        ) for idx in dic.keys()
-    ],
-)
-
-print("Records uploaded........................................")
-print("###########################################################")
-
-def chat(question):
-
-    hits = client.search(
-        collection_name="my_facts",
-        query_vector=encoder.encode(question).tolist(),
-        limit=3
-    )
-    context = []
-    for hit in hits:
-      context.append(list(hit.payload.values())[0])
-    
-    context = context[0] + context[1] + context[2]
-
-    system_prompt = """You are a helpful assistant, you will use the provided context to answer user questions.
-    Read the given context before answering questions and think step by step. If you can not answer a user question based on 
-    the provided context, inform the user. Do not use any other information for answering user. Provide a detailed answer to the question."""
-
-
-    B_INST, E_INST = "[INST]", "[/INST]"
-
-    B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-    SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
-
-    instruction = f""" 
-    Context: {context}
-    User: {question}"""
-
-    prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
-
-    result = llm(prompt_template)
-    return result 
-
-
-screen = gr.Interface(
-    fn = chat,
-    inputs = gr.Textbox(lines = 10, placeholder = "Enter your question here 👉"),
-    outputs = gr.Textbox(lines = 10, placeholder = "Your answer will be here soon 🚀"),
-    title="Q&N with PDF 👩🏻‍💻📓✍🏻💡",
-    description="This app facilitates a conversation with PDFs available on https://www.delo.si/assets/media/other/20110728/100%20Weird%20Facts%20About%20the%20Human%20Body.pdf💡",
-    theme="soft",
-    # examples=["Hello", "what is the speed of human nerve impulses?"],
-)
-
-screen.launch()