Update app.py

app.py

@@ -53,66 +53,66 @@ loaded_model = load_trained_model()
 
 st.title("🧠 Wall Defect Classification & AI-Based Description")
 category_choice = st.selectbox("🛠️ Select Defect Category Type:", ["Flooring"], index=0)
-
-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>"
-        )
+if category_choice == "Flooring":
+    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%}")
     
-        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,
+        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>"
             )
-            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}")
+        
+            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}")