Add application file

app.py

@@ -0,0 +1,83 @@
+import streamlit as st
+import fitz  # PyMuPDF
+from sentence_transformers import SentenceTransformer, util
+import faiss
+from transformers import pipeline
+
+st.title("Évaluation Stagiaire Data Scientist")
+
+uploaded_file = st.file_uploader("Choisissez un fichier PDF", type="pdf")
+
+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):
+    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_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:
+    with open("uploaded_document.pdf", "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    document_text = extract_text_from_pdf("uploaded_document.pdf")
+    
+    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)