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import streamlit as st
import fitz # PyMuPDF
from sentence_transformers import SentenceTransformer, util
import faiss
from transformers import pipeline
import os
from pathlib import Path
st.title("Évaluation Stagiaire Data Scientist")
uploaded_file = st.file_uploader("Choisissez un fichier PDF", type="pdf")
def save_uploaded_file(uploaded_file, directory):
directory = Path(directory)
directory.mkdir(parents=True, exist_ok=True)
file_path = directory / uploaded_file.name
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
return file_path
def extract_text_from_pdf(pdf_path):
text = ""
pdf_document = fitz.open(pdf_path)
for page_num in range(pdf_document.page_count):
page = pdf_document.load_page(page_num)
text += page.get_text()
return text
def index_document(text):
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
documents = [text]
document_embeddings = model.encode(documents, convert_to_tensor=True)
index = faiss.IndexFlatL2(document_embeddings.shape[1])
index.add(document_embeddings.cpu().detach().numpy())
faiss.write_index(index, 'document_index.faiss')
def get_answer_from_document(question, context):
qa_pipeline = pipeline('question-answering', model='deepset/roberta-base-squad2')
result = qa_pipeline(question=question, context=context)
return result
def generate_questions(text, num_questions=5, num_beams=5):
question_generation_pipeline = pipeline("text2text-generation", model="valhalla/t5-base-qg-hl")
input_text = "generate questions: " + text
questions = question_generation_pipeline(input_text, max_length=512, num_beams=num_beams, num_return_sequences=num_questions)
return [q['generated_text'] for q in questions]
def evaluate_responses(user_responses, correct_answers):
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
user_embeddings = model.encode(user_responses, convert_to_tensor=True)
correct_embeddings = model.encode(correct_answers, convert_to_tensor=True)
scores = []
for user_emb, correct_emb in zip(user_embeddings, correct_embeddings):
score = util.pytorch_cos_sim(user_emb, correct_emb)
scores.append(score.item())
return scores
def generate_training_plan(scores, threshold=0.7):
plan = []
for idx, score in enumerate(scores):
if score < threshold:
plan.append(f"Revoir la section correspondant à la question {idx+1}")
else:
plan.append(f"Passer à l'étape suivante après la question {idx+1}")
return plan
if uploaded_file is not None:
file_path = save_uploaded_file(uploaded_file, "uploaded_documents")
st.write(f"Fichier téléchargé et sauvegardé sous : {file_path}")
document_text = extract_text_from_pdf(file_path)
st.write("Texte extrait du document PDF:")
st.write(document_text[:1000]) # Affiche les 1000 premiers caractères du texte extrait
index_document(document_text)
st.subheader("Questions générées")
questions = generate_questions(document_text, num_questions=5)
for idx, question in enumerate(questions):
st.write(f"Question {idx+1}: {question}")
st.subheader("Évaluer les réponses de l'utilisateur")
user_responses = [st.text_input(f"Réponse de l'utilisateur {idx+1}") for idx in range(5)]
if st.button("Évaluer"):
correct_answers = ["La réponse correcte 1", "La réponse correcte 2", "La réponse correcte 3", "La réponse correcte 4", "La réponse correcte 5"]
scores = evaluate_responses(user_responses, correct_answers)
for idx, score in enumerate(scores):
st.write(f"Question {idx+1}: Score {score:.2f}")
st.subheader("Plan de formation personnalisé")
training_plan = generate_training_plan(scores)
for step in training_plan:
st.write(step)
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