Update app.py

app.py

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+import streamlit as st
+import base64
+import openai
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from keras.preprocessing.image import img_to_array
+from keras.applications.inception_v3 import preprocess_input
+import os
+from PIL import Image
+import io
+st.set_page_config(page_title="Wall Defect Classifier", layout="centered")
+
+# Set OpenAI API Key
+openai.api_key = os.getenv('OPEN_AI')
+
+# Defect categories
+class_labels = [
+    "Floor Cracks",
+    "Floor Peel",
+    "Shade Fading",
+    "Air Bubble (Single Defect)",
+    "Spike Roller (Single Defect)"
+]
+
+@st.cache_resource
+def load_trained_model():
+    return load_model('my_inceptionmodelwithoutaug (1).h5')
+
+def compress_image(image_bytes, max_size_kb=500):
+    img = Image.open(io.BytesIO(image_bytes))
+    quality = 95
+    output_bytes = io.BytesIO()
+    while True:
+        output_bytes.seek(0)
+        output_bytes.truncate()
+        img.save(output_bytes, format='JPEG', quality=quality)
+        if len(output_bytes.getvalue()) <= max_size_kb * 1024 or quality <= 5:
+            break
+        quality -= 5
+    return output_bytes.getvalue()
+
+def process_image_for_openai(image_bytes):
+    compressed_image = compress_image(image_bytes)
+    return base64.b64encode(compressed_image).decode('utf-8')
+
+# Load model once
+loaded_model = load_trained_model()
+
+# App UI
+
+st.title("🧠 Wall Defect Classification & AI-Based Description")
+st.markdown("Upload a wall surface image to detect potential defects and generate a structured AI analysis.")
+
+uploaded_file = st.file_uploader("📤 Upload an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    file_bytes = uploaded_file.getvalue()
+
+    st.image(file_bytes, caption="🖼️ Uploaded Image", use_column_width=True)
+
+    # Preprocess for prediction
+    input_img = cv2.imdecode(np.frombuffer(file_bytes, np.uint8), cv2.IMREAD_COLOR)
+    input_img_resized = cv2.resize(input_img, (256, 256))
+    x = img_to_array(input_img_resized)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+
+    preds = loaded_model.predict(x)
+    class_index = np.argmax(preds[0])
+    max_probability = preds[0][class_index]
+    class_name = class_labels[class_index]
+
+    # Classification Result Display
+    st.subheader("🔍 Classification Result")
+    st.success(f"**Predicted Defect:** {class_name}")
+    st.progress(min(int(max_probability * 100), 100))
+    st.markdown(f"**Confidence Level:** {max_probability:.2%}")
+
+    if max_probability < 0.59:
+        st.warning("⚠️ The confidence score is below 59%. Please manually verify this result.")
+    else:
+        compressed_base64 = process_image_for_openai(file_bytes)
+        ai_prompt = (
+             f"Our trained model predicts the following defect: {class_name}. "
+            f"Can you analyze the following image and generate AI-based descriptions "
+           
+            f"for this defect? The output format should be:\n"
+            f"Category ID: <Insert Category ID>\n"
+            f"Title: <Short Title of the Defect>\n"
+            f"Description: <A concise, technical description in 100 words or less>"
+        )
+    
+        st.subheader("Generating AI-Based Analysis...")
+        try:
+            response = openai.ChatCompletion.create(
+                model="gpt-4o",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": ai_prompt},
+                            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{compressed_base64}"}}
+                        ]
+                    }
+                ],
+                max_tokens=300,
+            )
+            ai_description = response.choices[0].message.content
+            st.subheader("📝 AI-Generated Defect Description")
+            st.text_area("Output", value=ai_description.strip(), height=250)
+    except Exception as e:
+        st.error(f"❌ An error occurred while generating the description:\n{e}")