Update app.py

import streamlit as st
from transformers import pipeline
import matplotlib.pyplot as plt
from PIL import Image, ImageFilter
import numpy as np
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import img_to_array

# Chargement du modèle d'analyse de sentiments et de reconnaissance d'images
sentiment_pipeline = pipeline("sentiment-analysis")
image_model = MobileNetV2(weights='imagenet')

def plot_sentiment(sentiment):
""" Dessine un graphique en camembert pour l'analyse de sentiments. """
labels = ['Positive', 'Negative']
sizes = [sentiment['score'], 1 - sentiment['score']]
colors = ['#ff9999','#66b3ff']
fig, ax = plt.subplots()
ax.pie(sizes, colors=colors, labels=labels, autopct='%1.1f%%', startangle=90)
ax.axis('equal')
return fig

def classify_image(image):
""" Classifie l'image et retourne les prédictions. """
image = image.resize((224, 224))
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
preds = image_model.predict(image)
return decode_predictions(preds, top=3)[0]

def apply_filters(image):
""" Applique des filtres à l'image et retourne l'image filtrée. """
filter = st.sidebar.selectbox("Sélectionnez un filtre", ["Original", "Flou", "Contours", "Accentuation"])
if filter == "Flou":
return image.filter(ImageFilter.BLUR)
elif filter == "Contours":
return image.filter(ImageFilter.CONTOUR)
elif filter == "Accentuation":
return image.filter(ImageFilter.EDGE_ENHANCE)
return image

# Configuration de l'application
st.title("Mon Application de Reconnaissance d'Images et d'Analyse de Sentiments")
st.sidebar.title("Options")

# Barre de navigation
options = st.sidebar.radio("Choisissez une fonctionnalité:", ['Reconnaissance d\'Images', 'Analyse de Sentiments'])

# Reconnaissance d'images
if options == 'Reconnaissance d\'Images':
uploaded_file = st.file_uploader("Téléchargez une image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
filtered_image = apply_filters(image)
st.image(filtered_image, caption='Image téléchargée', use_column_width=True)

# Classification de l'image
st.write("Classifying...")
labels = classify_image(filtered_image)
for label in labels:
st.write(f"{label[1]} ({label[2]*100:.2f}%)")

# Analyse de sentiments basée sur les étiquettes de l'image
sentiments = [sentiment_pipeline(label[1])[0] for label in labels]
for sentiment in sentiments:
st.write(f"Sentiment pour '{sentiment['label']}' : {sentiment['score']:.2f}")
fig = plot_sentiment(sentiment)
st.pyplot(fig)

# Analyse de sentiments
elif options == 'Analyse de Sentiments':
user_input = st.text_area("Entrez le texte à analyser", "Tapez ici...")
if st.button("Analyser"):
result = sentiment_pipeline(user_input)[0]
st.write(f"Sentiment: {result['label']}, Score: {result['score']:.2f}")
fig = plot_sentiment(result)
st.pyplot(fig)

app.py

@@ -1,49 +1,78 @@
 import streamlit as st
-import pandas as pd
-from sklearn import datasets
-from sklearn.ensemble import RandomForestClassifier
+from transformers import pipeline
+import matplotlib.pyplot as plt
+from PIL import Image, ImageFilter
+import numpy as np
+from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing.image import img_to_array
 
-st.write("""
-# Simple Iris Flower Prediction App
+# Chargement du modèle d'analyse de sentiments et de reconnaissance d'images
+sentiment_pipeline = pipeline("sentiment-analysis")
+image_model = MobileNetV2(weights='imagenet')
 
-This app predicts the **Iris flower** type!
-""")
+def plot_sentiment(sentiment):
+    """ Dessine un graphique en camembert pour l'analyse de sentiments. """
+    labels = ['Positive', 'Negative']
+    sizes = [sentiment['score'], 1 - sentiment['score']]
+    colors = ['#ff9999','#66b3ff']
+    fig, ax = plt.subplots()
+    ax.pie(sizes, colors=colors, labels=labels, autopct='%1.1f%%', startangle=90)
+    ax.axis('equal')
+    return fig
 
-st.sidebar.header('User Input Parameters')
+def classify_image(image):
+    """ Classifie l'image et retourne les prédictions. """
+    image = image.resize((224, 224))
+    image = img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+    image = preprocess_input(image)
+    preds = image_model.predict(image)
+    return decode_predictions(preds, top=3)[0]
 
-def user_input_features():
-    sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4)
-    sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4)
-    petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3)
-    petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2)
-    data = {'sepal_length': sepal_length,
-            'sepal_width': sepal_width,
-            'petal_length': petal_length,
-            'petal_width': petal_width}
-    features = pd.DataFrame(data, index=[0])
-    return features
+def apply_filters(image):
+    """ Applique des filtres à l'image et retourne l'image filtrée. """
+    filter = st.sidebar.selectbox("Sélectionnez un filtre", ["Original", "Flou", "Contours", "Accentuation"])
+    if filter == "Flou":
+        return image.filter(ImageFilter.BLUR)
+    elif filter == "Contours":
+        return image.filter(ImageFilter.CONTOUR)
+    elif filter == "Accentuation":
+        return image.filter(ImageFilter.EDGE_ENHANCE)
+    return image
 
-df = user_input_features()
+# Configuration de l'application
+st.title("Mon Application de Reconnaissance d'Images et d'Analyse de Sentiments")
+st.sidebar.title("Options")
 
-st.subheader('User Input parameters')
-st.write(df)
+# Barre de navigation
+options = st.sidebar.radio("Choisissez une fonctionnalité:", ['Reconnaissance d\'Images', 'Analyse de Sentiments'])
 
-iris = datasets.load_iris()
-X = iris.data
-Y = iris.target
+# Reconnaissance d'images
+if options == 'Reconnaissance d\'Images':
+    uploaded_file = st.file_uploader("Téléchargez une image...", type=["jpg", "png", "jpeg"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        filtered_image = apply_filters(image)
+        st.image(filtered_image, caption='Image téléchargée', use_column_width=True)
+        
+        # Classification de l'image
+        st.write("Classifying...")
+        labels = classify_image(filtered_image)
+        for label in labels:
+            st.write(f"{label[1]} ({label[2]*100:.2f}%)")
 
-clf = RandomForestClassifier()
-clf.fit(X, Y)
+        # Analyse de sentiments basée sur les étiquettes de l'image
+        sentiments = [sentiment_pipeline(label[1])[0] for label in labels]
+        for sentiment in sentiments:
+            st.write(f"Sentiment pour '{sentiment['label']}' : {sentiment['score']:.2f}")
+            fig = plot_sentiment(sentiment)
+            st.pyplot(fig)
 
-prediction = clf.predict(df)
-prediction_proba = clf.predict_proba(df)
-
-st.subheader('Class labels and their corresponding index number')
-st.write(iris.target_names)
-
-st.subheader('Prediction')
-st.write(iris.target_names[prediction])
-#st.write(prediction)
-
-st.subheader('Prediction Probability')
-st.write(prediction_proba)
+# Analyse de sentiments
+elif options == 'Analyse de Sentiments':
+    user_input = st.text_area("Entrez le texte à analyser", "Tapez ici...")
+    if st.button("Analyser"):
+        result = sentiment_pipeline(user_input)[0]
+        st.write(f"Sentiment: {result['label']}, Score: {result['score']:.2f}")
+        fig = plot_sentiment(result)
+        st.pyplot(fig)