Update app.py

app.py

@@ -1,14 +1,123 @@
 import gradio as gr
+import replicate
+import openai
 import trimesh
 import numpy as np
 from PIL import Image
+import requests
+import io
 import tempfile
+import os
+
+# Set API tokens
+os.environ["REPLICATE_API_TOKEN"] = "r8_Pc64F8EPrJ6PiNIIvaBUZcOGmiLC3Jp1gELYB"
+# Initialize the Replicate client
+rep_client = replicate.Client()
+
+# Set your OpenAI API key
+OPENAI_API_KEY = "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"
+openai.api_key = OPENAI_API_KEY
+
+# Initialize the Replicate client
+rep_client = replicate.Client()
+
+#--------------------------------2D Defect Simulator----------------------
+
+predefined_defects = [
+    "Missing bolts on railway track",
+    "Cracks on railway track",
+    "Overgrown vegetation near railway track",
+    "Broken railings on railway bridge",
+    "Debris on railway track",
+    "Damaged railway platform"
+]
+
+# Material defects structure
+material_defects = {
+    "Steel": ["Rust and Corrosion", "Pitting Corrosion", "Surface Cracks", "Wear Patterns", "Spalling", "Scaling"],
+    "Glass": ["Cracks", "Chips", "Scratches", "Frosting"],
+    "Aluminum": ["Corrosion", "Scratches and Dents", "Anodizing Wear"],
+    "Wood": ["Rot and Decay", "Cracks and Splits", "Weathering"],
+    "Plastics and Polymers": ["Cracking and Crazing", "UV Degradation", "Heat Distortion"],
+    "Rubber": ["Cracking", "Hardening and Brittleness", "Surface Wear"],
+    "Composite Materials": ["Delamination", "Impact Damage", "Fiber Wearing"],
+    "Ceramics": ["Crackling", "Chipping and Pitting", "Glaze Deterioration"]
+}
+
+# Function to ask rail defect question
+def ask_rail_defect_question(question):
+    structured_prompt = f"Translate the following user input into a concise, detailed visual description for a 3D model based on this input: '{question}'. Focus only on the defect’s appearance, texture qualities, and visual effects it would have on the material. Start the description directly with no extra words."
+    response = openai.ChatCompletion.create(
+        model='gpt-3.5-turbo',
+        messages=[
+            {"role": "system", "content": "Provide a concise, detailed visual description of the material's defect texture, focusing on visual and tactile qualities. Do not include any additional context or introductory phrases. Imagine the textures on railway components, but describe only the texture and material."},
+            {"role": "user", "content": structured_prompt}
+        ],
+    )
+    refined_description = response.choices[0].message['content']
+    return refined_description.strip()
+
+# Function to generate images from prompts
+def generate_images(prompt):
+    prediction = rep_client.predictions.create(
+        version="ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4",
+        input={"prompt": prompt}
+    )
+    prediction.wait()
+    if prediction.status == "succeeded":
+        image_url = prediction.output[0]
+        response = requests.get(image_url)
+        image = Image.open(io.BytesIO(response.content))
+        image.save("defect.png")
+        return image
+    return None
+
+# Function to create data URL from PIL image
+def image_to_data_url(pil_image):
+    buffered = io.BytesIO()
+    pil_image.save(buffered, format="JPEG")
+    base64_image = base64.b64encode(buffered.getvalue()).decode('utf-8')
+    return f"data:image/jpeg;base64,{base64_image}"
+
+# Function to inpaint images
+def inpaint_texture(image, prompt):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    
+    image_data_url = image_to_data_url(image)
+       
+    input = {
+      "image": image_data_url,
+      "prompt": prompt,
+      "scheduler": "K_EULER_ANCESTRAL",
+      "num_outputs": 1,
+      "guidance_scale": 7.5,
+      "num_inference_steps": 100,
+      "image_guidance_scale": 1.5
+    }
+
+    prediction = rep_client.predictions.create(
+        version="30c1d0b916a6f8efce20493f5d61ee27491ab2a60437c13c588468b9810ec23f",
+        input = input
+    )
+    prediction.wait()
+    if prediction.status == "succeeded":
+        image_url = prediction.output[0]
+        response = requests.get(image_url)
+        image = Image.open(io.BytesIO(response.content))
+        image.save("defect.png")
+        return image
+    return None
+
+# Function to update defect options
+def update_defect_options(selected_material):
+    return gr.update(value='', choices=material_defects[selected_material])
 
 # Function to visualize texture based on selection criteria
 def visualize_dynamic_texture(predefined_section, x_min, x_max, y_min, y_max, z_min, z_max):
     # Load the original mesh
     mesh = trimesh.load('train.glb', force='mesh')
-    rust_texture = Image.open('rust_steel.png').convert('RGB')
+    rust_texture = Image.open('defect.png').convert('RGB')
 
     # Predefined sections
     if predefined_section == 'right compartments':
@@ -86,4 +195,118 @@ with gr.Blocks() as app:
     for input_component in inputs:
         input_component.change(fn=update_model, inputs=inputs, outputs=modified_model)
 
-app.launch()
+    gr.Markdown("### 3D Defect Simulator Tabs")
+    with gr.Tabs():
+        with gr.Tab("Predefined Defect Texture"):
+            with gr.Row():
+                material_input = gr.Dropdown(choices=list(material_defects.keys()), label="Select Material")
+                defect_input = gr.Dropdown(choices=[], label="Select Defect Type")
+                generate_button = gr.Button("Generate Texture")
+            image_output = gr.Image(label="Generated Texture")
+            model_output_predefined = gr.Model3D(label="3D Model with Applied Texture")
+
+            material_input.change(fn=update_defect_options, inputs=[material_input], outputs=[defect_input])
+            generate_button.click(
+                fn=lambda material, defect: generate_images(ask_rail_defect_question(f"Describe the texture of {defect} on {material}")),
+                inputs=[material_input, defect_input],
+                outputs=[image_output]
+            )
+
+            visualize_button_predefined = gr.Button("Visualize 3D Model")
+            visualize_button_predefined.click(
+                fn=update_model,
+                inputs=[section_dropdown, x_min_slider, x_max_slider, y_min_slider, y_max_slider, z_min_slider, z_max_slider],
+                outputs=[model_output_predefined]
+            )
+
+        with gr.Tab("Custom Defect Texture"):
+            with gr.Row():
+                custom_prompt_input = gr.Textbox(label="Enter Custom Prompt for Texture", placeholder="Describe any texture detail you need.")
+                refine_button = gr.Button("Refine Prompt")
+            refined_prompt_output = gr.Textbox(label="Refined Prompt", placeholder="This will show the refined prompt.")
+
+            with gr.Row():
+                generate_button = gr.Button("Generate Texture")
+            custom_image_output = gr.Image(label="Generated Texture")
+            model_output_custom = gr.Model3D(label="3D Model with Applied Texture")
+
+            # Refine the input prompt
+            refine_button.click(
+                fn=lambda prompt: ask_rail_defect_question(prompt),
+                inputs=[custom_prompt_input],
+                outputs=[refined_prompt_output]
+            )
+
+            # Use the refined prompt to generate the texture image
+            generate_button.click(
+                fn=lambda prompt: generate_images(prompt),
+                inputs=[refined_prompt_output],
+                outputs=[custom_image_output]
+            )
+
+            visualize_button_custom = gr.Button("Visualize 3D Model")
+            visualize_button_custom.click(
+                fn=update_model,
+                inputs=[section_dropdown, x_min_slider, x_max_slider, y_min_slider, y_max_slider, z_min_slider, z_max_slider],
+                outputs=[model_output_custom]
+            )
+
+        with gr.Tab("Inpaint Defect Texture"):
+            with gr.Row():
+                image_input = gr.Image(label="Upload Image for Inpainting")
+                inpaint_prompt_input = gr.Textbox(label="Enter Prompt for Texture Inpainting")
+                inpaint_button = gr.Button("Generate Inpainted Texture")
+            inpaint_image_output = gr.Image(label="Generated Inpainted Texture")
+            model_output_inpaint = gr.Model3D(label="3D Model with Applied Texture")
+
+            # Use the images and prompt to generate the inpainted texture image
+            inpaint_button.click(
+                fn=lambda img, prompt: inpaint_texture(img, prompt),
+                inputs=[image_input, inpaint_prompt_input],
+                outputs=[inpaint_image_output]
+            )
+
+            visualize_button_inpaint = gr.Button("Visualize 3D Model")
+            visualize_button_inpaint.click(
+                fn=update_model,
+                inputs=[section_dropdown, x_min_slider, x_max_slider, y_min_slider, y_max_slider, z_min_slider, z_max_slider],
+                outputs=[model_output_inpaint]
+            )
+
+        with gr.Tab("2D Defect Simulator"):
+            with gr.Tabs():
+                with gr.Tab("Current Defects"):
+                    with gr.Row():
+                        prompt_input = gr.Dropdown(choices=predefined_defects, label="Select a prompt")
+                        number_input_dropdown = gr.Number(label="Number of images to generate", value=1, minimum=1, maximum=10)
+                        submit_button_dropdown = gr.Button("Generate")
+                    image_outputs_dropdown = gr.Gallery()
+
+                    def on_submit_click_dropdown(prompt, number_of_images):
+                        images = process_railway_defects(prompt, number_of_images)
+                        return images
+
+                    submit_button_dropdown.click(
+                        fn=on_submit_click_dropdown,
+                        inputs=[prompt_input, number_input_dropdown],
+                        outputs=image_outputs_dropdown
+                    )
+
+                with gr.Tab("Custom Defect"):
+                    with gr.Row():
+                        custom_prompt_input = gr.Textbox(label="Custom Defect")
+                        number_input_custom = gr.Number(label="Number of images to generate", value=1, minimum=1, maximum=10)
+                        submit_button_custom = gr.Button("Generate")
+                    image_outputs_custom = gr.Gallery()
+
+                    def on_submit_click_custom(custom_prompt, number_of_images):
+                        images = process_railway_defects(custom_prompt, number_of_images)
+                        return images
+
+                    submit_button_custom.click(
+                        fn=on_submit_click_custom,
+                        inputs=[custom_prompt_input, number_input_custom],
+                        outputs=image_outputs_custom
+                    )
+
+app.launch()