Delete app.py

app.py

@@ -1,109 +0,0 @@
-import gradio as gr
-from gradio_pdf import PDF
-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.llms import LlamaCpp 
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.vectorstores import Qdrant
-from transformers import AutoModelForCausalLM
-
-# Load the embedding model
-encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
-print("Embedding model loaded...")
-
-# Load the LLM
-callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
-'''
-llm = AutoModelForCausalLM.from_pretrained(
-    "TheBloke/Llama-2-7B-Chat-GGUF",
-    model_file="llama-2-7b-chat.Q3_K_S.gguf",
-    model_type="llama",
-    temperature=0.2,
-    repetition_penalty=1.5,
-    max_new_tokens=300,
-)
-'''
-llm = LlamaCpp(
-    model_path="./llama-2-7b-chat.Q3_K_S.gguf",
-    temperature = 0.2,
-    n_ctx=2048,
-    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
-    max_tokens = 500,
-    callback_manager=callback_manager,
-    verbose=True,
-)
-
-print("LLM loaded...")
-
-client = QdrantClient(path="./db")
-
-def setup_database(files):
-    all_chunks = []
-    for file in files:
-        pdf_path = file
-        reader = PdfReader(pdf_path)
-        text = "".join(page.extract_text() for page in reader.pages if page.extract_text())
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=50, length_function=len)
-        chunks = text_splitter.split_text(text)
-        all_chunks.extend(chunks)
-    
-    print(f"Total chunks: {len(all_chunks)}")
-    
-    client.recreate_collection(
-        collection_name="my_facts",
-        vectors_config=models.VectorParams(
-            size=encoder.get_sentence_embedding_dimension(),
-            distance=models.Distance.COSINE,
-        ),
-    )
-    
-    print("Collection created...")
-
-    for idx, chunk in enumerate(all_chunks):
-        client.upload_record(
-            collection_name="my_facts",
-            record=models.Record(
-                id=idx,
-                vector=encoder.encode(chunk).tolist(),
-                payload={"text": chunk}
-            )
-        )
-    
-    print("Records uploaded...")
-
-def answer(question):
-    hits = client.search(
-        collection_name="my_facts",
-        query_vector=encoder.encode(question).tolist(),
-        limit=3
-    )
-    
-    context = " ".join(hit.payload["text"] for hit in hits)
-    system_prompt = "You are a helpful co-worker. Use the provided context to answer user questions. Do not use any other information."
-    prompt = f"Context: {context}\nUser: {question}\n{system_prompt}"
-    response = llm(prompt)
-    return response
-
-def chat(messages):
-    if not messages:
-        return "Please upload PDF documents to initialize the database."
-    last_message = messages[-1]
-    return answer(last_message["message"])
-
-screen = gr.Interface(
-    fn=chat,
-    inputs=gr.Textbox(placeholder="Type your question here..."),
-    outputs="chatbot",
-    title="Q&A with PDFs 👩🏻‍💻📓✍🏻💡",
-    description="This app facilitates a conversation with PDFs uploaded💡",
-    theme="soft",
-    live=True,
-    allow_screenshot=False,
-    allow_flagging=False,
-)
-
-# Add a way to upload and setup the database before starting the chat
-screen.launch()