From ff19702c1d8e1de08b8e4ae4b3334df8fbdb736f Mon Sep 17 00:00:00 2001
From: Sayandip2023 <138033617+Sayandip2023@users.noreply.github.com>
Date: Mon, 29 Jul 2024 10:40:44 +0530
Subject: [PATCH] Add files via upload

---
 app.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 app.py

diff --git a/app.py b/app.py
new file mode 100644
index 0000000..ea0b0be
--- /dev/null
+++ b/app.py
@@ -0,0 +1,40 @@
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from keras.preprocessing.image import img_to_array, load_img
+import time
+
+# Load the trained model
+model = tf.keras.models.load_model('Sentiment.h5')
+
+# Define class labels
+classes = ['Disappointed', 'interested', 'neutral']
+
+# Function to preprocess image
+def preprocess_image(image):
+    image = image.resize((64, 64))
+    image_array = img_to_array(image) / 255.0
+    return np.expand_dims(image_array, axis=0)
+
+# Streamlit application
+st.title("Sentiment Analysis using Computer Vision")
+st.write("Upload an image to get sentiment classification")
+
+uploaded_file = st.file_uploader("Upload the image here", type="jpg")
+
+if uploaded_file is not None:
+    # Load and preprocess image
+    image = load_img(uploaded_file)
+    preprocessed_image = preprocess_image(image)
+
+    # Predict
+    start_time = time.time()
+    predictions = model.predict(preprocessed_image)
+    inference_time = time.time() - start_time
+
+    # Display results
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write(f"Inference Time: {inference_time:.4f} seconds")
+
+    predicted_class = np.argmax(predictions, axis=1)[0]
+    st.write(f"Detected emotion: {classes[predicted_class]}")