Upload first huggingface test file on space

This is the first test file upload on hugging face space to use spaces for the first time.

grado-test-3-huggingface-space.py

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+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
+
+
+
+# loading the embedding model - 
+
+encoder = SentenceTransformer("all-MiniLM-L6-v2")
+
+print("embedding model loaded.............................")
+print("####################################################")
+
+# loading the LLM 
+
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+print("loading the LLM......................................")
+
+llm = LlamaCpp(
+ model_path="/home/devangpagare/llm/models/llama-2-7b-chat.Q3_K_S.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,
+)
+print("LLM loaded........................................")
+print("################################################################")
+
+def get_chunks(text):
+ text_splitter = RecursiveCharacterTextSplitter(
+ # seperator = "\n",
+ chunk_size = 500,
+ chunk_overlap = 100,
+ length_function = len,
+ )
+
+ chunks = text_splitter.split_text(text)
+ return chunks
+
+
+pdf_path = '/home/devangpagare/llm/qdrant-cloud-rag-main/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 are ready.....................................")
+print("######################################################")
+
+qdrant = QdrantClient(path = "/home/devangpagare/llm/qdrant-cloud-rag-main/gradio/db")
+print("db created................................................")
+print("#####################################################################")
+
+qdrant.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)
+
+qdrant.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):
+ # question = input("ask question from pdf.....")
+
+
+ hits = qdrant.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 
+
+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?"],
+ cache_examples=True,
+).launch(share = True, auth=("username", "password"))