Delete app.py

app.py

@@ -1,196 +0,0 @@
-import argparse
-# from dataclasses import dataclass
-from langchain_community.vectorstores import Chroma
-#from langchain_openai import OpenAIEmbeddings
-#from langchain_openai import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-
-
-# a template by which the bot will answer the quetion according the "context" of
-# the text that will be imported as context later, determines the information that the question should be answered according to.
-PROMPT_TEMPLATE = """
-Answer the question based only on the following context:
-
-{context}
-
----
-
-Answer the question based on the above context: {question}
-"""
-# from langchain.document_loaders import DirectoryLoader
-from langchain_community.document_loaders import DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.schema import Document
-# from langchain.embeddings import OpenAIEmbeddings
-#from langchain_openai import OpenAIEmbeddings
-from langchain_community.vectorstores import Chroma
-import openai
-from dotenv import load_dotenv
-import os
-import shutil
-# a custom embedding
-from sentence_transformers import SentenceTransformer
-from langchain_experimental.text_splitter import SemanticChunker
-from typing import List
-
-
-class MyEmbeddings:
-    def __init__(self):
-        self.model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        return [self.model.encode(t).tolist() for t in texts]
-    def embed_query(self, query: str) -> List[float]:
-            return [self.model.encode([query])][0][0].tolist()
-
-
-embeddings = MyEmbeddings()
-
-splitter = SemanticChunker(embeddings)
-CHROMA_PATH = "chroma8"
-# call the chroma generated in a directory
-db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
-
-
-import os
-from huggingface_hub import login
-
-# Retrieve the token from the environment variable
-token = os.getenv('HF_Token')
-
-# Log in using the token
-login(token=token)
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "tiiuae/falcon-7b-instruct" # meta-llama/Llama-2-7b-chat-hf
-
-tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)
-
-
-
-
-from transformers import pipeline
-
-llama_pipeline = pipeline(
-    "text-generation",  # LLM task
-    model=model,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-
-
-
-def get_response(prompt: str) -> None:
-    """
-    Generate a response from the Llama model.
-
-    Parameters:
-        prompt (str): The user's input/question for the model.
-
-    Returns:
-        None: Prints the model's response.
-    """
-    sequences = llama_pipeline(
-        prompt,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-        max_length=256,
-    )
-    print("Chatbot:", sequences[0]['generated_text'])
-
-
-
-
-template = """Answer the query based only the provided context, and if the answer is not contained within the context below, say "I don't knowwwww"
-
-Context:
-{context}
-
-{query}""".strip()
-
-from langchain.prompts import PromptTemplate
-
-prompt_template = PromptTemplate(
-    input_variables=["query", "context"],
-    template=template
-)
-
-
-
-
-
-
-
-
-
-
-
-# Generate a response from the Llama model
-def get_llama_response(message: str, history: list) -> str:
-    """
-    Generates a conversational response from the Llama model.
-
-    Parameters:
-        message (str): User's input message.
-        history (list): Past conversation history.
-
-    Returns:
-        str: Generated response from the Llama model.
-    """
-    print('messageeeeeeeeeeeeeee:',message)
-    #query = format_message(message, history)
-    response = ""
-
-    query = """
-    Answer the question based only on the following context. Dont provide any information out of the context:
-
-    {context}
-
-    ---
-
-    Answer the question based on the above context: {question}
-    """
-
-    #message='how does alice meet the mad hatter?'
-    ######################
-    # Search the DB for similar documents to the query.
-    print('before searching inside the db')
-    results = db.similarity_search_with_relevance_scores(message, k=3)
-    if len(results) == 0 or results[0][1] < 0.5:
-        print(f"Unable to find matching results.")
-    print('after searchingf insidee the db')
-
-
-    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
-    #context_text="amin is a math student."
-    ####################3
-
-    query = prompt_template.format(query=message, context=context_text)
-    #query=query.format(context=context_text,question=message)
-    print('im gonna generate response')
-    sequences = llama_pipeline(
-        query,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-        max_length=1024,
-    )
-
-    print('igenerated response')
-    generated_text = sequences[0]['generated_text']
-    response = generated_text[len(query):]  # Remove the prompt from the output
-
-    print("Chatbot:", response.strip())
-    print('i wanneea return')
-    return response.strip()
-
-
-
-import gradio as gr
-
-gr.ChatInterface(get_llama_response).launch()