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Browse files- __pycache__/functions.cpython-310.pyc +0 -0
- app.py +4 -4
- functions.py +2 -2
- text_to_speech.mp3 +0 -0
__pycache__/functions.cpython-310.pyc
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Binary files a/__pycache__/functions.cpython-310.pyc and b/__pycache__/functions.cpython-310.pyc differ
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app.py
CHANGED
@@ -46,7 +46,7 @@ with col2:
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button = st.button("Prédire", use_container_width=True)
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if button:
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predicted, confidence = predict(uploaded_file)
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diagnosis_prompt = get_diagnosis(predicted, confidence)
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diagnosis = generate_text(diagnosis_prompt)
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summary = summarize_text(diagnosis)
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translation = translate_fr_to_es(summary)
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@@ -59,6 +59,7 @@ if uploaded_file and translation:
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<div class="container card border-0 shadow-sm p-4 mt-2">
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<div class="card-body">
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<h2 class="gradient-text"> Resultat de la prédiction </h2>
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<p class="lead"> {summary} </p>
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</div>
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</div>
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@@ -71,6 +72,7 @@ if uploaded_file and translation:
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""",
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unsafe_allow_html=True,
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)
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st.markdown(
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f"""
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@@ -83,9 +85,7 @@ if uploaded_file and translation:
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""",
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unsafe_allow_html=True,
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)
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-
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with col2:
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st.audio(output_path, format="audio/mp3")
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# Afficher le pied de page
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show_footer(linkedin_image_base64, github_image_base64, huggingface_image_base64)
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button = st.button("Prédire", use_container_width=True)
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if button:
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predicted, confidence = predict(uploaded_file)
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diagnosis_prompt, label = get_diagnosis(predicted, confidence)
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diagnosis = generate_text(diagnosis_prompt)
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summary = summarize_text(diagnosis)
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translation = translate_fr_to_es(summary)
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<div class="container card border-0 shadow-sm p-4 mt-2">
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<div class="card-body">
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<h2 class="gradient-text"> Resultat de la prédiction </h2>
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<h4 class="gradient-text text-underline"> Type de tumeur: {label} </h3>
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<p class="lead"> {summary} </p>
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</div>
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</div>
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""",
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unsafe_allow_html=True,
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)
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st.audio(output_path, format="audio/mp3")
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st.markdown(
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f"""
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""",
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unsafe_allow_html=True,
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)
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+
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# Afficher le pied de page
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show_footer(linkedin_image_base64, github_image_base64, huggingface_image_base64)
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functions.py
CHANGED
@@ -23,8 +23,8 @@ def summarize_text(text):
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def get_diagnosis(prediction, confidence):
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labels = ["glioma", "meningioma", "notumor", "pituitary"]
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result = labels[prediction]
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prompt = f'Tu es un médecin qui s\'appelle Skym
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return prompt
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def generate_text(input_text, max_retries=3):
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def get_diagnosis(prediction, confidence):
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labels = ["glioma", "meningioma", "notumor", "pituitary"]
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result = labels[prediction]
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prompt = f'Tu es un médecin (neurologue) qui s\'appelle Skym qui doit annoncer à un patient X le type de tumeur qu\'il a. Le type de tumeur prédit est "{result}" avec une confiance de {confidence}. Veuillez fournir un diagnostic concis, précis et des conseils.'
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return prompt, result
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def generate_text(input_text, max_retries=3):
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text_to_speech.mp3
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Binary files a/text_to_speech.mp3 and b/text_to_speech.mp3 differ
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