Upload 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
+
+openai.api_key =  os.getenv('OPENAI_API_KEY')
+
+class_labels = [
+    "Algae",
+    "Bubbles and blisters",
+    "Cracks",
+    "Efflorescence",
+    "Fungus",
+    "Patchiness",
+    "Peeling",
+    "Poor Hiding",
+    "Shade Variation"
+]
+
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
+@st.cache_resource
+def load_trained_model():
+    return load_model('my_model12.h5')
+
+loaded_model = load_trained_model()
+
+st.title("Wall Defect Classification and AI Analysis")
+st.write("Upload an image to classify wall defects and generate AI-based descriptions.")
+
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+    # Read and preprocess the input image
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    input_img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+    input_img_resized = cv2.resize(input_img, dsize=(256, 256), interpolation=cv2.INTER_CUBIC)
+
+    x = img_to_array(input_img_resized)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+
+    preds = loaded_model.predict(x)
+
+    threshold = 0.3
+
+    class_indices = np.where(preds[0] > threshold)[0]
+    class_probabilities = preds[0][class_indices]
+
+    results_text = ""
+    predicted_defects = []
+    if len(class_indices) > 0:
+        for i, class_idx in enumerate(class_indices):
+            class_name = class_labels[class_idx]
+            results_text += f"{class_name} (Class {class_idx}): Probability {class_probabilities[i]:.2f}\n"
+            predicted_defects.append(class_name)
+    else:
+        results_text = "No classes detected with a probability greater than the threshold."
+
+    # Display classification results in a text box
+    st.text_area("Classification Results:", value=results_text, height=200)
+
+    # Encode the uploaded image as Base64
+    base64_image = base64.b64encode(file_bytes).decode("utf-8")
+    image_data = f"data:image/jpeg;base64,{base64_image}"
+
+    # Generate AI-based descriptions using OpenAI API
+    if predicted_defects:
+        defects_string = ", ".join(predicted_defects)
+        ai_prompt = (
+            f"Our trained model predicts the following defects: {defects_string}. "
+            f"Can you analyze the following image and generate AI-based descriptions for these defects?"
+        )
+
+        st.write("Analyzing image")
+        try:
+            response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+               {
+                "role": "user",
+                "content": [
+                    {"type": "text", 
+                     "text":ai_prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}",
+                        },
+                    },
+                 ],
+             }
+           ],
+             max_tokens=300,
+
+    )
+            # Extract AI-generated descriptions
+            ai_description = response.choices[0].message.content
+            st.text_area("AI-Generated Description:", value=ai_description, height=200)
+
+        except Exception as e:
+            st.error(f"An error occurred while generating AI-based descriptions: {str(e)}")
+    else:
+        st.warning("No defects detected. AI analysis skipped.")