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bd8906d 59ac75a bd8906d 9de4707 bd8906d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import os
import tempfile
import gradio as gr
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.retrieval_qa.base import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_community.vectorstores import Chroma
from langchain.llms.base import LLM
from groq import Groq
from typing import Any, List, Optional
# Set up Groq client
GROQ_API_KEY = "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"
os.environ["GROQ_API_KEY"] = GROQ_API_KEY
groq_client = Groq(api_key=GROQ_API_KEY)
# Custom LLM class for Groq
class GroqLLM(LLM):
client: Any
model: str = "llama3-8b-8192"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
chat_completion = self.client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model=self.model,
)
return chat_completion.choices[0].message.content
@property
def _llm_type(self) -> str:
return "groq"
# Initialize GroqLLM
llm = GroqLLM(client=groq_client)
# Custom prompt template
template = """You are ACReSAL AI Chatbot, a direct and concise assistant. A large language model trained by Brilliant Esystems Limited. Answer the question using only the information provided in the context. Give only the specific answer requested.
Context: {context}
Question: {question}
Answer:"""
PROMPT = PromptTemplate(
template=template, input_variables=["context", "question"]
)
class PDFQuestionAnswering:
def __init__(self):
self.qa_system = None
def setup_qa_system(self, pdf_file):
# Create a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(pdf_file)
temp_file_path = temp_file.name
# Load PDF
loader = PyPDFLoader(temp_file_path)
documents = loader.load()
# Remove the temporary file
os.unlink(temp_file_path)
# Text splitting
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
# Embeddings
embeddings = HuggingFaceEmbeddings()
# Vector store
docsearch = Chroma.from_documents(texts, embeddings)
# Set up RetrievalQA
self.qa_system = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(),
chain_type_kwargs={"prompt": PROMPT}
)
return "PDF processed successfully. You can now ask questions."
def get_answer(self, question):
if self.qa_system is None:
return "Please upload a PDF file first."
raw_answer = self.qa_system.run(question)
return raw_answer.strip()
pdf_qa = PDFQuestionAnswering()
def process_pdf(pdf_file):
if pdf_file is None:
return "Please upload a PDF file."
return pdf_qa.setup_qa_system(pdf_file)
def answer_question(question):
return pdf_qa.get_answer(question)
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Agro-Climatic Resilience in Semi-Arid Landscapes - ACReSAL AI Powered Chatbot ")
with gr.Row():
pdf_input = gr.File(label="Upload PDF", type="binary", file_types=[".pdf"])
pdf_output = gr.Textbox(label="PDF Processing Status")
pdf_button = gr.Button("Process PDF")
with gr.Row():
question_input = gr.Textbox(label="Enter your question")
answer_output = gr.Textbox(label="Answer")
question_button = gr.Button("Get Answer")
pdf_button.click(process_pdf, inputs=[pdf_input], outputs=[pdf_output])
question_button.click(answer_question, inputs=[question_input], outputs=[answer_output])
if __name__ == "__main__":
demo.launch()
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