added features

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pyc filter=lfs diff=lfs merge=lfs -text

app.py

@@ -2,10 +2,13 @@ import streamlit as st
 import numpy as np
 import cv2
 from PIL import Image
-import torchvision.transforms as transforms
+import torch
+import torch.nn as nn
+from torchvision import transforms, models
 from segment_anything import sam_model_registry, SamPredictor
 from io import BytesIO
 import base64
+import requests
 from google.generativeai import configure, GenerativeModel
 
 # Configure Gemini API
@@ -14,53 +17,57 @@ model = GenerativeModel("gemini-2.0-flash")
 
 # Load SAM model with ViT-Base
 def load_sam_model():
-    sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b.pth")  # Use vit_b instead of vit_h
+    sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b.pth")
     predictor = SamPredictor(sam)
     return predictor
 
 # Preprocess image using OpenCV (Edge Detection & Background Removal)
 def preprocess_image(image):
-    image = np.array(image.convert("RGB"))  # Convert to NumPy array
-    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  # Convert to Grayscale
-    edges = cv2.Canny(gray, 100, 200)  # Apply Canny Edge Detection
-    return Image.fromarray(edges)  # Convert back to PIL Image
+    image = np.array(image.convert("RGB"))
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    edges = cv2.Canny(gray, 100, 200)
+    return Image.fromarray(edges)
 
 # Segment garment using SAM
 def segment_garment(image, predictor):
     image_np = np.array(image.convert("RGB"))
     predictor.set_image(image_np)
 
-    # Use center point of image as prompt
     height, width, _ = image_np.shape
     input_point = np.array([[width // 2, height // 2]])
-    input_label = np.array([1])  # 1 indicates object selection
+    input_label = np.array([1])
 
     masks, _, _ = predictor.predict(point_coords=input_point, point_labels=input_label)
-    mask = masks[0]  # Get first mask
-
-    # Resize mask to match image
+    mask = masks[0]
     mask_resized = cv2.resize(mask.astype(np.uint8) * 255, (width, height), interpolation=cv2.INTER_NEAREST)
-    mask_resized = np.stack([mask_resized] * 3, axis=-1)  # Convert to 3-channel
+    mask_resized = np.stack([mask_resized] * 3, axis=-1)
 
-    # Apply segmentation mask
     segmented = np.where(mask_resized > 0, image_np, 0)
-
     return Image.fromarray(segmented)
 
 # AI garment analysis
-def analyze_garment(image):
+def analyze_garment(image, style_pref=None, feedback=None, generate_variations=False):
     image_bytes = BytesIO()
     image.save(image_bytes, format="PNG")
     encoded_image = base64.b64encode(image_bytes.getvalue()).decode("utf-8")
 
+    style_input = f"\nStyle preference: {style_pref}" if style_pref else ""
+    feedback_input = f"\nUser feedback: {feedback}" if feedback else ""
+    variation_input = "\nGenerate 3 variations of this design based on the given preferences." if generate_variations else ""
+
     prompt = {
         "parts": [
-            {"text": "Analyze the garment in this image, describing its style, fabric, and design elements. "
-                     "Suggest the best occasions to wear it and recommend complementary fashion pieces."},
+            {
+                "text": (
+                    "Analyze the garment in this image, describing its style, fabric, and design elements. "
+                    "Suggest the best occasions to wear it and recommend complementary fashion pieces." +
+                    style_input + feedback_input + variation_input
+                )
+            },
             {"inline_data": {"mime_type": "image/png", "data": encoded_image}}
         ]
     }
-    
+
     response = model.generate_content(prompt)
     return response.text if response else "Analysis failed."
 
@@ -68,42 +75,51 @@ def analyze_garment(image):
 sam_predictor = load_sam_model()
 
 # Streamlit UI
-st.title("👗 AI Fashion Analysis with SAM & Gemini AI")
+st.title("👗 AI Fashion Analysis & Design Studio")
 
-# Description and Instructions
 st.markdown("""
 ### 📌 How to Use this App:
-1. *Upload an Image*: Click the upload button and select a fashion image.
-2. *Preprocess the Image*: Click 'Preprocess' to apply edge detection and garment segmentation.
-3. *View Results*: Processed and segmented images will be displayed.
-4. *Analyze the Garment*: Click 'Analyze Garment' to get AI-based fashion insights.
+1. Upload a fashion image or sketch.
+2. Preprocess to see edge detection and garment segmentation.
+3. Provide your style preferences or feedback.
+4. Analyze and generate new fashion ideas using AI!
 """)
 
-# File Upload
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+uploaded_file = st.file_uploader("📤 Upload a fashion image (PNG, JPG, JPEG)", type=["jpg", "png", "jpeg"])
 
-if uploaded_file is not None:
+if uploaded_file:
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_container_width=True)
+    st.image(image, caption="🖼️ Uploaded Image", use_container_width=True)
 
-    # Initialize session state for persistence
+    # Session state setup
     if "processed_image" not in st.session_state:
         st.session_state.processed_image = None
     if "segmented_image" not in st.session_state:
         st.session_state.segmented_image = None
 
-    # Preprocess Button (Runs Edge Detection & Segmentation)
-    if st.button("Preprocess"):
+    if st.button("⚙️ Preprocess Image"):
         st.session_state.processed_image = preprocess_image(image)
         st.session_state.segmented_image = segment_garment(image, sam_predictor)
 
-    # Display Preprocessed Images if Available
     if st.session_state.processed_image:
-        st.image(st.session_state.processed_image, caption="Edge Detection", use_container_width=True)
+        st.image(st.session_state.processed_image, caption="🧠 Edge Detection", use_container_width=True)
     if st.session_state.segmented_image:
-        st.image(st.session_state.segmented_image, caption="Segmented Garment", use_container_width=True)
+        st.image(st.session_state.segmented_image, caption="✂️ Segmented Garment", use_container_width=True)
+
+    # 🧑‍🤝‍🧑 Human-AI Interaction
+    st.markdown("### 🧠 Customize Your Style & Get AI Suggestions")
+    style_pref = st.text_input("🔖 Describe your style preference (e.g., elegant, streetwear, minimalist)")
+    feedback = st.text_input("🗣️ Provide feedback to AI (e.g., Make it more vibrant, Use denim)")
 
-    # Analyze Garment Button (Gemini AI)
-    if st.button("Analyze Garment"):
-        result = analyze_garment(image)
-        st.success(result)
+    generate_variations = st.checkbox("🎨 Generate design variations")
+    
+    if st.button("🤖 Analyze & Collaborate with AI"):
+        result = analyze_garment(image, style_pref, feedback, generate_variations)
+        st.success(result)
+
+        # Fabric suggestion extraction (Optional basic parsing)
+        st.markdown("### 🧵 Suggested Fabrics / Materials:")
+        for line in result.splitlines():
+            if "fabric" in line.lower() or "material" in line.lower():
+                st.info(line)
+