uplaod app.py

app.py

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+# -*- coding: utf-8 -*-
+"""RAGLLAMA.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1z-I8OtD-uFaX9KHENLECbxMmPPuPDNSp
+"""
+
+pip install transformers==4.37.2 optimum==1.12.0 --quiet
+pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ --quiet
+pip install langchain==0.1.9 --quiet
+#  !pip install chromadb
+pip install sentence_transformers==2.4.0 --quiet
+pip install unstructured --quiet
+pip install pdf2image --quiet
+pip install pdfminer.six==20221105 --quiet
+pip install unstructured-inference --quiet
+pip install faiss-gpu==1.7.2 --quiet
+pip install pikepdf==8.13.0 --quiet
+pip install pypdf==4.0.2 --quiet
+pip install pillow_heif==0.15.0 --quiet
+
+from langchain.llms import HuggingFacePipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
+
+model_name = "TheBloke/Llama-2-13b-Chat-GPTQ"
+
+model = AutoModelForCausalLM.from_pretrained(model_name,
+                                             device_map="auto",
+                                             trust_remote_code=True)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+gen_cfg = GenerationConfig.from_pretrained(model_name)
+gen_cfg.max_new_tokens=512
+gen_cfg.temperature=0.0000001 # 0.0
+gen_cfg.return_full_text=True
+gen_cfg.do_sample=True
+gen_cfg.repetition_penalty=1.11
+
+pipe=pipeline(
+    task="text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    generation_config=gen_cfg
+)
+
+llm = HuggingFacePipeline(pipeline=pipe)
+
+"""Testing LLM with Prompt Structure
+
+"""
+
+from textwrap import fill
+from langchain.prompts import PromptTemplate
+
+template = "Question: {text}"
+
+prompt = PromptTemplate(
+    input_variables=["text"],
+    template=template,
+)
+
+text = "how a coffee bean is roasted?"
+result = llm.invoke(prompt.format(text=text))
+print(fill(result.strip(), width=100))
+
+import locale
+locale.getpreferredencoding = lambda: "UTF-8"
+
+"""Load PDF"""
+
+from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.vectorstores.utils import filter_complex_metadata  # 'filter_complex_metadata' removes complex metadata that are not in str, int, float or bool format
+
+# Replace 'your_pdf_file.pdf' with the name of your uploaded PDF file
+pdf_loader = UnstructuredPDFLoader('coffee.pdf')
+pdf_doc = pdf_loader.load()
+updated_pdf_doc = filter_complex_metadata(pdf_doc)
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=128)
+chunked_pdf_doc = text_splitter.split_documents(updated_pdf_doc)
+len(chunked_pdf_doc)
+
+from langchain.embeddings import HuggingFaceEmbeddings
+embeddings = HuggingFaceEmbeddings()
+
+"""Creating Vector Database with FAISS (Similarity search)"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# 
+# %%time
+# # Create the vectorized db with FAISS
+# from langchain.vectorstores import FAISS
+# db_pdf = FAISS.from_documents(chunked_pdf_doc, embeddings)
+# 
+# # Create the vectorized db with Chroma
+# # from langchain.vectorstores import Chroma
+# # db_pdf = Chroma.from_documents(chunked_pdf_doc, embeddings)
+
+import transformers
+transformers.logging.set_verbosity_error()
+
+"""RetrivalQA - LLM Prompt Structure"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# from langchain.prompts import PromptTemplate
+# from langchain.chains import RetrievalQA
+# 
+# # use the recommended propt style for the LLAMA 2 LLM
+# prompt_template = """
+# [INST] <>
+# Use the following context to Answer the question at the end. Do not use any other information. If you can't find the relevant information in the context, just say you don't have enough information to answer the question. Don't try to make up an answer.
+# 
+# <>
+# 
+# {context}[/INST]
+# 
+# Question: {question}
+# """
+# 
+# 
+# prompt = PromptTemplate(template=prompt_template, input_variables=["context","question"])
+# Chain_pdf = RetrievalQA.from_chain_type(
+#     llm=llm,
+#     chain_type="stuff",
+#     # retriever=db.as_retriever(search_type="similarity_score_threshold", search_kwargs={'k': 5, 'score_threshold': 0.8})
+#     # Similarity Search is the default way to retrieve documents relevant to a query, but we can use MMR by setting search_type = "mmr"
+#     # k defines how many documents are returned; defaults to 4.
+#     # score_threshold allows to set a minimum relevance for documents returned by the retriever, if we are using the "similarity_score_threshold" search type.
+#     # return_source_documents=True, # Optional parameter, returns the source documents used to answer the question
+#     retriever=db_pdf.as_retriever(), # (search_kwargs={'k': 5, 'score_threshold': 0.8}),
+#     chain_type_kwargs={"prompt": prompt },
+# )
+# query = "what is a coffee?"
+# result = Chain_pdf.invoke(query)
+# result_text = result['result']
+# answer_start_index = result_text.find("Question:")  # Find the start index of the answer
+# answer = result_text[answer_start_index:].strip()  # Extract the answer
+# print(fill(answer, width=100))
+#
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# query = "explain about coffee brewing?"
+# result = Chain_pdf.invoke(query)
+# result_text = result['result']
+# answer_start_index = result_text.find("Question:")
+# answer = result_text[answer_start_index:].strip()
+# print(fill(answer, width=100))
+
+"""Hallucination Test
+
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# query = "How to make a milk tea?"
+# result = Chain_pdf.invoke(query)
+# result_text = result['result']
+# answer_start_index = result_text.find("Question:")
+# answer = result_text[answer_start_index:].strip()
+# print(fill(answer, width=100))
+
+pip install gradio transformers
+
+import gradio as gr
+
+def query_qa(query):
+    result = Chain_pdf.invoke(query)
+    result_text = result['result']
+    answer_start_index = result_text.find("Question:")
+    answer = result_text[answer_start_index:].strip()
+    return answer
+
+qa_interface = gr.Interface(query_qa, "text", "text", title="Coffee Q&A")
+
+qa_interface.launch()
+debug=True
+