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README.md

@@ -1,13 +1,53 @@
 ---
-title: StyleSavy
-emoji: 📉
-colorFrom: blue
+title: StyleSavvy
+emoji: 🏆
+colorFrom: gray
 colorTo: purple
 sdk: gradio
 sdk_version: 5.29.0
 app_file: app.py
 pinned: false
-short_description: 'StyleSavvy is an AI-powered virtual stylist that analyzes '
+short_description: Style Savvy - AI Style Fashion Consultant
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# 👗 StyleSavvy — AI Fashion Consultant
+
+StyleSavvy is an AI-powered virtual stylist that uses computer vision and natural language generation to give personalized fashion advice.
+
+## ✨ Features
+- Detects clothing from uploaded photos using YOLOS-Fashionpedia
+- Removes background for better detection (optional)
+- Provides tailored styling tips based on:
+  - Body type
+  - Face shape
+  - Gender
+  - Occasion
+- Uses `google/flan-t5-large` to generate expert-level suggestions
+
+## 📸 How to Use
+1. Upload a clear photo of your outfit
+2. Select your body type, face shape, and gender
+3. Enter the event or occasion
+4. Click **Generate Style Tips**
+5. Enjoy your personalized fashion advice! 🪞
+
+## 🛠️ Tech Stack
+- Gradio UI
+- Hugging Face Transformers
+- YOLOS object detection
+- FLAN-T5 language model
+- remove.bg API for optional background removal
+
+## 🧠 Example Use Case
+> “A curvy woman with a round face going to a summer wedding”  
+> → StyleSavvy suggests breathable floral fabrics, statement earrings, and pastel tones that match the event ambiance.
+
+## 🔐 API Key
+Make sure to add a Hugging Face **Secret**:
+- `REMOVE_BG_API_KEY`: your remove.bg key
+
+---
+
+🚀 Try it live: [StyleSavvy on Hugging Face](https://huggingface.co/spaces/Munazz/StyleSavvy)

models/llm.py

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+
+from transformers import pipeline
+from typing import List
+
+PROMPTS = {
+    "category_expansion": (
+        "As a top-tier fashion advisor, craft one impactful styling suggestion for a {gender} individual with a {body_type} body "
+        "and {face_shape} face attending the {occasion}. They have on {items}. "
+        "Highlight a strategic enhancement in silhouette, color scheme, accessory choice, or footwear to elevate their look."
+    ),
+    "event_aesthetic": (
+        "Imagine you are curating an immersive style experience for a {gender} attendee with a {body_type} silhouette and {face_shape} face at the {occasion}. "
+        "They’re currently wearing {items}. Provide one highly descriptive recommendation that harmonizes fabric textures, color temperature, silhouette, and accessory accents with the event’s specific ambiance, lighting conditions, and seasonal atmosphere."
+    ),
+    "fashion_editor": (
+        "You are the Editor-in-Chief of a prestigious fashion publication. Advise a {gender} trendsetter with a {body_type} frame and {face_shape} face attending the {occasion}, "
+        "currently in {items}. Offer one magazine-cover-worthy styling tip—highlight a trending color palette, editorial-worthy silhouette, and innovative accessory placement that will resonate with a discerning audience."
+    ),
+    "influencer_style": (
+        "As a cutting-edge style influencer with millions of followers, recommend one eye-catching flair tip for a {gender} follower with a {body_type} physique and {face_shape} face, "
+        "heading to the {occasion} in {items}. Frame it as a social-media-caption-ready moment: mention a statement accessory, bold color pop, or texture twist that will go viral."
+    ),
+    "seasonal_trend": (
+        "As a seasonal style expert specializing in spring/summer trends, guide a {gender} individual with a {body_type} shape and {face_shape} face preparing for the {occasion}. "
+        "They currently wear {items}. Provide one tip incorporating current seasonal motifs—think floral prints, breathable linens, or eco-friendly fabrics—that elevates their ensemble."
+    ),
+}
+
+class StyleSavvy:
+    def __init__(
+        self,
+        model_name: str = "google/flan-t5-large",
+        device: int   = -1,     # -1 = CPU, or GPU index
+        max_length: int = 150,
+    ):
+        # A local instruction-tuned T5 model
+        self.pipe = pipeline(
+            "text2text-generation",
+            model=model_name,
+            tokenizer=model_name,
+            device=device,
+        )
+        self.max_length = max_length
+        self.num_beams = 4
+    # TODO: Modification: Add more prompts to the advise function
+    # to make it more specific to the user's needs.
+    # The function now takes in the user's body type, face shape, and occasion
+    # and generates style tips accordingly.
+
+    def advise(self,
+               items: List[str],
+               body_type: str,
+               face_shape: str,
+               gender: str,
+               occasion: str
+    ) -> List[str]:
+        """
+        Generate one result per prompt template and return all as a list.
+        """
+        labels = ", ".join(items) if items else "an outfit"
+        results: List[str] = []
+        for tpl in PROMPTS.values():
+            prompt = tpl.format(
+                body_type=body_type,
+                face_shape=face_shape,
+                gender = gender,
+                occasion=occasion,
+                items=labels
+            )
+            out = self.pipe(
+                prompt,
+                max_length=self.max_length,
+                num_beams=self.num_beams,
+                early_stopping=True,
+                do_sample=False,
+                no_repeat_ngram_size=3,  # avoid repeating phrases
+            )[0]["generated_text"].strip()
+            results.append(out)
+        return results
+
+
+
+

models/vision.py

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+
+# models/vision.py -- Working
+
+from transformers import pipeline
+from PIL import Image
+
+class VisionModel:
+    def __init__(
+        self,
+        model_name: str = "valentinafeve/yolos-fashionpedia",
+        threshold: float = 0.7
+    ):
+        self.pipe = pipeline("object-detection", model=model_name)
+        self.threshold = threshold
+
+    def detect(self, image: Image.Image):
+        # 1) Ensure RGB
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+
+        # 2) Run detection
+        results = self.pipe(image)
+
+        # 3) Process & filter
+        processed = []
+        for r in results:
+            score = float(r["score"])
+            if score < self.threshold:
+                continue
+
+            # r["box"] is a dict: {"xmin":..., "ymin":..., "xmax":..., "ymax":...}
+            box = r["box"]
+            coords = [
+                float(box["xmin"]),
+                float(box["ymin"]),
+                float(box["xmax"]),
+                float(box["ymax"]),
+            ]
+
+            processed.append({
+                "label": r["label"],
+                "score": score,
+                "box": coords
+            })
+
+        return processed
+
+
+
+

utils.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d50e1f3884c180574825b8583b5807222caef442523680315924f44ba2ecac40
+size 10368