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import os
import tempfile
import gradio as gr
from PIL import Image
from pdf2image import convert_from_path
import pytesseract
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq
class ChatbotModel:
def __init__(self):
os.environ["GROQ_API_KEY"] = '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'
self.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'},
encode_kwargs={'normalize_embeddings': True}
)
self.llm = ChatGroq(
model='llama3-70b-8192',
temperature=0.5,
max_tokens=None,
timeout=None,
max_retries=2,
)
self.memory = ConversationBufferMemory(memory_key="history", input_key="question")
self.template = """You are an intelligent assistant... (Rest of your prompt as is)"""
self.QA_CHAIN_PROMPT = PromptTemplate(
input_variables=["history", "context", "question"],
template=self.template
)
self.db1 = None
self.qa_chain = None
def ocr_image(self, image_path, language='eng+guj'):
img = Image.open(image_path)
return pytesseract.image_to_string(img, lang=language)
def ocr_pdf(self, pdf_path, language='eng+guj'):
images = convert_from_path(pdf_path)
return "\n".join([pytesseract.image_to_string(img, lang=language) for img in images])
def process_file(self, uploaded_file):
_, file_extension = os.path.splitext(uploaded_file.name)
file_extension = file_extension.lower()
with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as temp_file:
temp_file.write(uploaded_file.read())
temp_path = temp_file.name
if file_extension == '.pdf':
raw_text = self.ocr_pdf(temp_path, language='guj+eng')
elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
raw_text = self.ocr_image(temp_path, language='guj+eng')
else:
return "Unsupported file format."
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
text_chunks = text_splitter.split_text(raw_text)
self.db1 = FAISS.from_documents(text_chunks, self.embeddings)
self.qa_chain = RetrievalQA.from_chain_type(
self.llm,
retriever=self.db1.as_retriever(),
chain_type='stuff',
verbose=True,
chain_type_kwargs={
"verbose": True,
"prompt": self.QA_CHAIN_PROMPT,
"memory": self.memory
}
)
return "File processed successfully!"
def get_response(self, user_input):
if not self.qa_chain:
return "Please upload and process a file before asking questions."
response = self.qa_chain({"query": user_input})
return response["result"]
chatbot = ChatbotModel()
def upload_and_process(file):
return chatbot.process_file(file)
def ask_question(question):
return chatbot.get_response(question)
interface = gr.Blocks()
with interface:
gr.Markdown("# Educational Chatbot with Document Analysis")
with gr.Row():
file_upload = gr.File(label="Upload PDF or Image")
upload_btn = gr.Button("Process File")
output = gr.Textbox(label="File Processing Status")
with gr.Row():
question_box = gr.Textbox(label="Ask a Question")
ask_btn = gr.Button("Submit")
answer = gr.Textbox(label="Answer")
upload_btn.click(upload_and_process, inputs=file_upload, outputs=output)
ask_btn.click(ask_question, inputs=question_box, outputs=answer)
interface.launch()
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