Update Home.py

Home.py

@@ -1,22 +1,57 @@
+import os
+os.system("pip install tensorflow")
+os.system("pip install scikit-learn")
+
 import streamlit as st
-from transformers import pipeline
+import tensorflow as tf
+import numpy as np
+import pickle
+from PIL import Image
 
-# Load the text generation pipeline
-@st.cache_resource
-def load_model():
-    return pipeline("text-generation", model="tiiuae/falcon-7b-instruct")
+# Constants
+MODEL_PATH = "image_classification.h5"
+LABEL_ENCODER_PATH = "le.pkl"
+EXPECTED_SIZE = (64, 64)  # Update this based on your model's input shape
 
-model = load_model()
+def load_resources():
+    """Load model and label encoder."""
+    model = tf.keras.models.load_model(MODEL_PATH)
+    with open(LABEL_ENCODER_PATH, "rb") as f:
+        label_encoder = pickle.load(f)
+    return model, label_encoder
 
-# Streamlit UI
-st.title("Convolution_Neural_Network")
+# Load resources
+model, label_encoder = load_resources()
+
+def preprocess_image(image):
+    """Resize image to match model input shape."""
+    image = image.resize(EXPECTED_SIZE)  # Resize to match model input
+    image_array = np.array(image)  # Convert to numpy array
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
 
-# User input
-user_input = st.text_area("Enter a prompt:", "Once upon a time...")
+def predict(image):
+    """Predict the class of the uploaded image."""
+    image_array = preprocess_image(image)
+    preds = model.predict(image_array)
+    class_index = np.argmax(preds)
+    return label_encoder.inverse_transform([class_index])[0]
+
+# Streamlit UI
+st.set_page_config(page_title="Image Classifier", layout="wide")
+st.markdown("""
+    <h1 style='text-align: center; color: #4A90E2;'>🖼️ Image Classification App</h1>
+    <p style='text-align: center; font-size: 18px;'>Upload an image and let our model classify it for you!</p>
+    <hr>
+""", unsafe_allow_html=True)
 
-if st.button("Generate"):
-    with st.spinner("Generating text..."):
-        output = model(user_input, max_length=100, num_return_sequences=1)
-        st.success("Generated Text:")
-        st.write(output[0]['generated_text'])
+st.sidebar.header("Upload Your Image")
+uploaded_file = st.sidebar.file_uploader("Choose an image", type=["jpg", "png", "jpeg"], help="Supported formats: JPG, PNG, JPEG")
 
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="📸 Uploaded Image", use_column_width=True)
+    
+    if st.button("🔍 Classify Image", use_container_width=True):
+        prediction = predict(image)
+        st.success(f"🎯 Predicted Class: {prediction}")