Update app.py

app.py

@@ -1,70 +1,78 @@
 import gradio as gr
-from PIL import Image
+from PIL import Image, ImageDraw
 import requests
 from io import BytesIO
-import numpy as np
-import cv2
+import os
 
 # AI model repo for design generation
 repo = "artificialguybr/TshirtDesignRedmond-V2"
 
+# Generate plain cloth image with specified color
 def generate_cloth(color_prompt):
     prompt = f"A plain {color_prompt} colored T-shirt hanging on a plain wall."
     api_url = f"https://api-inference.huggingface.co/models/{repo}"
     headers = {}
-    payload = {"inputs": prompt, "parameters": {"num_inference_steps": 30}}
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "num_inference_steps": 30,
+        },
+    }
     response = requests.post(api_url, headers=headers, json=payload)
     if response.status_code == 200:
-        return Image.open(BytesIO(response.content)).convert("RGB")
+        return Image.open(BytesIO(response.content))
     else:
         raise Exception(f"Error generating cloth: {response.status_code}")
 
+# Generate design based on user prompt
 def generate_design(design_prompt):
     prompt = f"A bold {design_prompt} design with vibrant colors, highly detailed."
     api_url = f"https://api-inference.huggingface.co/models/{repo}"
     headers = {}
-    payload = {"inputs": prompt, "parameters": {"num_inference_steps": 30}}
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "num_inference_steps": 30,
+        },
+    }
     response = requests.post(api_url, headers=headers, json=payload)
     if response.status_code == 200:
-        return Image.open(BytesIO(response.content)).convert("RGBA")
+        return Image.open(BytesIO(response.content))
     else:
         raise Exception(f"Error generating design: {response.status_code}")
 
-def warp_design_to_tshirt(cloth_image, design_image, points):
-    cloth_np = np.array(cloth_image)
-    design_np = np.array(design_image)
-    if design_np.shape[2] == 4:
-        alpha_channel = design_np[:, :, 3] / 255.0
-        design_rgb = design_np[:, :, :3]
-    else:
-        alpha_channel = np.ones((design_np.shape[0], design_np.shape[1]))
-        design_rgb = design_np
+# Overlay design on cloth with adjustable width and height
+def overlay_design(cloth_image, design_image, x, y, width, height):
+    # Ensure images are in RGBA mode
+    cloth_image = cloth_image.convert("RGBA")
+    design_image = design_image.convert("RGBA")
 
-    src_points = np.float32([[0, 0], [design_np.shape[1], 0], [design_np.shape[1], design_np.shape[0]], [0, design_np.shape[0]]])
-    dest_points = np.float32(points)
-    matrix = cv2.getPerspectiveTransform(src_points, dest_points)
-    warped_design = cv2.warpPerspective(design_rgb, matrix, (cloth_np.shape[1], cloth_np.shape[0]))
-    warped_alpha = cv2.warpPerspective(alpha_channel, matrix, (cloth_np.shape[1], cloth_np.shape[0]))
+    # Resize design based on user inputs
+    resized_design = design_image.resize((width, height))
 
-    for c in range(3):
-        cloth_np[:, :, c] = (warped_alpha * warped_design[:, :, c] + (1 - warped_alpha) * cloth_np[:, :, c]).astype(np.uint8)
+    # Overlay the design at specified coordinates
+    result = cloth_image.copy()
+    result.paste(resized_design, (x, y), resized_design)
 
-    return Image.fromarray(cloth_np)
+    return result
 
+# Full workflow: Generate cloth, design, and combine them
 def design_tshirt(color_prompt, design_prompt, x, y, width, height):
     cloth_image = generate_cloth(color_prompt)
     design_image = generate_design(design_prompt)
-    points = [[x, y], [x + width, y], [x + width - 20, y + height], [x + 20, y + height]]
-    final_image = warp_design_to_tshirt(cloth_image, design_image, points)
+    final_image = overlay_design(cloth_image, design_image, x, y, width, height)
     return final_image
 
+# Gradio interface
 with gr.Blocks() as interface:
     gr.Markdown("# **AI Cloth Designer**")
+    gr.Markdown("Generate custom T-shirts by specifying a color and adding a draggable design with adjustable size.")
+
     with gr.Row():
         with gr.Column(scale=1):
             color_prompt = gr.Textbox(label="Cloth Color", placeholder="E.g., Red, Blue")
             design_prompt = gr.Textbox(label="Design Details", placeholder="E.g., Abstract art, Nature patterns")
-            x_coord = gr.Slider(label="X Coordinate", minimum=0, maximum=600, step=10, value=100)
+            x_coord = gr.Slider(label="X Coordinate", minimum=0, maximum=400, step=10, value=100)
             y_coord = gr.Slider(label="Y Coordinate", minimum=0, maximum=600, step=10, value=100)
             width_slider = gr.Slider(label="Design Width", minimum=100, maximum=500, step=10, value=200)
             height_slider = gr.Slider(label="Design Height", minimum=100, maximum=500, step=10, value=300)
@@ -78,4 +86,4 @@ with gr.Blocks() as interface:
         outputs=output_image,
     )
 
-interface.launch(debug=True)
+interface.launch(debug=True)