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.gitattributes

@@ -35,3 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 vendor-usecase-app/app/furniture/sofa/sofa-1.jpeg filter=lfs diff=lfs merge=lfs -text
 vendor-usecase-app/generated_image.png filter=lfs diff=lfs merge=lfs -text
+app/furniture/sofa/sofa-1.jpeg filter=lfs diff=lfs merge=lfs -text
+generated_image.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,55 @@
+# These are some examples of commonly ignored file patterns.
+# You should customize this list as applicable to your project.
+# Learn more about .gitignore:
+#     https://www.atlassian.com/git/tutorials/saving-changes/gitignore
+
+# Node artifact files
+node_modules/
+dist/
+
+# Compiled Java class files
+*.class
+
+# Compiled Python bytecode
+*.py[cod]
+
+# Log files
+*.log
+
+# Package files
+*.jar
+
+# Maven
+target/
+dist/
+
+# JetBrains IDE
+.idea/
+
+# Unit test reports
+TEST*.xml
+
+# Generated by MacOS
+.DS_Store
+
+# Generated by Windows
+Thumbs.db
+
+# Applications
+*.app
+*.exe
+*.war
+
+# Large media files
+*.mp4
+*.tiff
+*.avi
+*.flv
+*.mov
+*.wmv
+
+.env
+generated_image*
+sample_images/
+test.py
+pqr.py

README.md

@@ -1,13 +1,29 @@
----
-title: VTO
-emoji: ⚡
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# README #
+
+This README would normally document whatever steps are necessary to get your application up and running.
+
+### What is this repository for? ###
+
+* Quick summary
+* Version
+* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo)
+
+### How do I get set up? ###
+
+* Summary of set up
+* Configuration
+* Dependencies
+* Database configuration
+* How to run tests
+* Deployment instructions
+
+### Contribution guidelines ###
+
+* Writing tests
+* Code review
+* Other guidelines
+
+### Who do I talk to? ###
+
+* Repo owner or admin
+* Other community or team contact

app/api/routers/v1/background_edit.py

@@ -0,0 +1,70 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import BACKGROUND_EDIT_TEMPLATE 
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType,SurfaceType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/background_edit", tags=["Background_edit"])
+async def background_edit(
+    garment_type: Annotated[str, Form()],
+    surface_type: Annotated[str, Form()],
+    camera_view_angle: Annotated[str, Form()],
+    camera_distance_meters: Annotated[float, Form()],
+    camera_focal_length_mm: Annotated[float, Form()],
+    camera_aperture_f_number: Annotated[float, Form()],
+    camera_lighting_condition: Annotated[str, Form()],
+    camera_background: Annotated[str, Form()],
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    """
+    Generate a try-on image based on garment images, model characteristics, and camera settings.
+    """
+    try:
+        garment=GarmentType(garment_type=garment_type)
+        surface=SurfaceType(surface_type=surface_type)
+        camera_settings = CameraSchema(
+            view_angle=camera_view_angle,
+            distance_meters=camera_distance_meters,
+            focal_length_mm=camera_focal_length_mm,
+            aperture_f_number=camera_aperture_f_number,
+            lighting_condition=camera_lighting_condition,
+            background=camera_background
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid model or camera attributes: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=BACKGROUND_EDIT_TEMPLATE.format(
+            garment_type=garment.garment_type,
+            surface_type=surface.surface_type,
+            camera_view_angle=camera_settings.view_angle,
+            camera_distance_meters=camera_settings.distance_meters,
+            camera_focal_length_mm=camera_settings.focal_length_mm,
+            camera_aperture_f_number=camera_settings.aperture_f_number,
+            camera_lighting_condition=camera_settings.lighting_condition,
+            camera_background=camera_settings.background
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/detail_shots.py

@@ -0,0 +1,37 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import DETAIL_SHOTS_TEMPLATE 
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+        
+@router.post("/detail", tags=["details-shots"])
+async def detail(
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=DETAIL_SHOTS_TEMPLATE
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/editorial.py

@@ -0,0 +1,81 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import EDITORIAL_PROMPT_TEMPLATE
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/editorial", tags=["Editorial"])
+async def tryon(
+    model_gender: Annotated[str, Form()],
+    model_age_range: Annotated[str, Form()],
+    model_body_shape: Annotated[str, Form()],
+    model_race_ethnicity: Annotated[str, Form()],
+    model_pose: Annotated[str, Form()],
+    camera_view_angle: Annotated[str, Form()],
+    camera_distance_meters: Annotated[float, Form()],
+    camera_focal_length_mm: Annotated[float, Form()],
+    camera_aperture_f_number: Annotated[float, Form()],
+    camera_lighting_condition: Annotated[str, Form()],
+    camera_background: Annotated[str, Form()],
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    """
+    Generate a editorial style image based on garment images, model characteristics, and camera settings.
+    """
+    try:
+        model_attributes = ModelSchema(
+            gender=model_gender,
+            age_range=model_age_range,
+            body_shape=model_body_shape,
+            race_ethnicity=model_race_ethnicity,
+            pose=model_pose
+        )
+        camera_settings = CameraSchema(
+            view_angle=camera_view_angle,
+            distance_meters=camera_distance_meters,
+            focal_length_mm=camera_focal_length_mm,
+            aperture_f_number=camera_aperture_f_number,
+            lighting_condition=camera_lighting_condition,
+            background=camera_background
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid model or camera attributes: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=EDITORIAL_PROMPT_TEMPLATE.format(
+            model_gender=model_attributes.gender,
+            model_age_range=model_attributes.age_range,
+            model_race_ethnicity=model_attributes.race_ethnicity,
+            model_body_shape=model_attributes.body_shape,
+            model_pose=model_attributes.pose,
+            camera_view_angle=camera_settings.view_angle,
+            camera_distance_meters=camera_settings.distance_meters,
+            camera_focal_length_mm=camera_settings.focal_length_mm,
+            camera_aperture_f_number=camera_settings.aperture_f_number,
+            camera_lighting_condition=camera_settings.lighting_condition,
+            camera_background=camera_settings.background
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/health.py

@@ -0,0 +1,7 @@
+from fastapi import APIRouter
+
+router = APIRouter()
+
+@router.get("/health", tags=["Health"])
+async def health_check():
+    return {"status": "healthy", "message": "API is up and running!"}

app/api/routers/v1/mannequin.py

@@ -0,0 +1,37 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import MANNEQUIN_TEMPLATE 
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+        
+@router.post("/mannequin", tags=["MANNEQUIN"])
+async def mannequin(
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=MANNEQUIN_TEMPLATE
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/multi_view.py

@@ -0,0 +1,37 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import MULTI_VIEW_TEMPLATE 
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+        
+@router.post("/multi_view", tags=["Multi_view"])
+async def multi_view(
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=MULTI_VIEW_TEMPLATE
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/occasion.py

@@ -0,0 +1,84 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import OCCASION_BASED_PROMPT_TEMPLATE 
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/occasion", tags=["occasion-based-styling"])
+async def occasion_based(
+    garment_type: Annotated[str, Form()],
+    model_gender: Annotated[str, Form()],
+    model_age_range: Annotated[str, Form()],
+    model_body_shape: Annotated[str, Form()],
+    model_race_ethnicity: Annotated[str, Form()],
+    model_pose: Annotated[str, Form()],
+    camera_view_angle: Annotated[str, Form()],
+    camera_distance_meters: Annotated[float, Form()],
+    camera_focal_length_mm: Annotated[float, Form()],
+    camera_aperture_f_number: Annotated[float, Form()],
+    camera_lighting_condition: Annotated[str, Form()],
+    camera_background: Annotated[str, Form()],
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    # """
+    # Generate a try-on image based on garment images, model characteristics, and camera settings.
+    # """
+    try:
+        garment=GarmentType(garment_type=garment_type)
+        model_attributes = ModelSchema(
+            gender=model_gender,
+            age_range=model_age_range,
+            body_shape=model_body_shape,
+            race_ethnicity=model_race_ethnicity,
+            pose=model_pose
+        )
+        camera_settings = CameraSchema(
+            view_angle=camera_view_angle,
+            distance_meters=camera_distance_meters,
+            focal_length_mm=camera_focal_length_mm,
+            aperture_f_number=camera_aperture_f_number,
+            lighting_condition=camera_lighting_condition,
+            background=camera_background
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid model or camera attributes: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=OCCASION_BASED_PROMPT_TEMPLATE.format(
+            garment_type=garment.garment_type,
+            model_gender=model_attributes.gender,
+            model_age_range=model_attributes.age_range,
+            model_race_ethnicity=model_attributes.race_ethnicity,
+            model_body_shape=model_attributes.body_shape,
+            model_pose=model_attributes.pose,
+            camera_view_angle=camera_settings.view_angle,
+            camera_distance_meters=camera_settings.distance_meters,
+            camera_focal_length_mm=camera_settings.focal_length_mm,
+            camera_aperture_f_number=camera_settings.aperture_f_number,
+            camera_lighting_condition=camera_settings.lighting_condition,
+            camera_background=camera_settings.background
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/outfitmatch.py

@@ -0,0 +1,84 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.schemas.common_schemas import ModelSchema, CameraSchema, GarmentType
+from app.services.prompt_factory import OUTFIT_MATCH_PROMPT_TEMPLATE
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/outfit-match", tags=["Outfit Match"])
+async def outfit_match_tryon(
+    garment_type: Annotated[str, Form()],
+    model_gender: Annotated[str, Form()],
+    model_age_range: Annotated[str, Form()],
+    model_body_shape: Annotated[str, Form()],
+    model_race_ethnicity: Annotated[str, Form()],
+    model_pose: Annotated[str, Form()],
+    camera_view_angle: Annotated[str, Form()],
+    camera_distance_meters: Annotated[float, Form()],
+    camera_focal_length_mm: Annotated[float, Form()],
+    camera_aperture_f_number: Annotated[float, Form()],
+    camera_lighting_condition: Annotated[str, Form()],
+    camera_background: Annotated[str, Form()],
+    garment_images: List[UploadFile] = File(..., description="Exactly two images of the garments (e.g., dress and bag).", min_items=2, max_items=2),
+):
+    """
+    Generate a try-on image based on garment images, model characteristics, and camera settings.
+    """
+    try:
+        garment=GarmentType(garment_type=garment_type)
+        model_attributes = ModelSchema(
+            gender=model_gender,
+            age_range=model_age_range,
+            body_shape=model_body_shape,
+            race_ethnicity=model_race_ethnicity,
+            pose=model_pose
+        )
+        camera_settings = CameraSchema(
+            view_angle=camera_view_angle,
+            distance_meters=camera_distance_meters,
+            focal_length_mm=camera_focal_length_mm,
+            aperture_f_number=camera_aperture_f_number,
+            lighting_condition=camera_lighting_condition,
+            background=camera_background
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid model or camera attributes: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 2:
+        raise HTTPException(status_code=400, detail="Exactly two garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+
+    prompt=OUTFIT_MATCH_PROMPT_TEMPLATE.format(
+            garment_type=garment.garment_type,
+            model_gender=model_attributes.gender,
+            model_age_range=model_attributes.age_range,
+            model_race_ethnicity=model_attributes.race_ethnicity,
+            model_body_shape=model_attributes.body_shape,
+            model_pose=model_attributes.pose,
+            camera_view_angle=camera_settings.view_angle,
+            camera_distance_meters=camera_settings.distance_meters,
+            camera_focal_length_mm=camera_settings.focal_length_mm,
+            camera_aperture_f_number=camera_settings.aperture_f_number,
+            camera_lighting_condition=camera_settings.lighting_condition,
+            camera_background=camera_settings.background
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/size_comparision.py

@@ -0,0 +1,68 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import SIZE_COMPARISION_TEMPLATE, JEWELLERY_SIZE_COMPARISION_TEMPLATE
+from app.schemas.common_schemas import DimensionSchema,GarmentType
+import io
+from typing import Annotated, List, Optional
+
+router = APIRouter()
+
+@router.post("/size", tags=["size-comparision"])
+async def size(
+    garment_type: str = Form(...),
+    product_height: Optional[float] = Form(default=None),
+    product_width: Optional[float] = Form(default=None),
+    product_length: Optional[float] = Form(default=None),
+    garment_images: List[UploadFile] = File(..., description="Front-shot image of the handbag/jewellery", min_items=1, max_items=1),
+):
+    # """
+    # Generate what fits inside the bag
+    # """
+    try:
+        product_dimensions=DimensionSchema(
+            height_cm=product_height,
+            width_cm=product_width,
+            length_cm=product_length
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid dimensions: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one front shot of the image")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    if garment_type=="handbags":
+        prompt = SIZE_COMPARISION_TEMPLATE.format(
+            height_cm=product_dimensions.height_cm if product_dimensions.height_cm is not None else "N/A",
+            width_cm=product_dimensions.width_cm if product_dimensions.width_cm is not None else "N/A",
+            length_cm=product_dimensions.length_cm if product_dimensions.length_cm is not None else "N/A"
+        )
+
+    elif garment_type=="jewellery and watches":
+        prompt=JEWELLERY_SIZE_COMPARISION_TEMPLATE.format(
+            height_cm=product_dimensions.height_cm if product_dimensions.height_cm is not None else "N/A",
+            width_cm=product_dimensions.width_cm if product_dimensions.width_cm is not None else "N/A",
+            length_cm=product_dimensions.length_cm if product_dimensions.length_cm is not None else "N/A"
+        )
+    else:
+        raise HTTPException(status_code=400, detail=f"Invalid garment type")
+            
+
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/tryon.py

@@ -0,0 +1,84 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import LIFESTYLE_TRYON_PROMPT_TEMPLATE 
+from app.schemas.common_schemas import ModelSchema, CameraSchema,GarmentType
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/tryon", tags=["TRY-ON"])
+async def tryon(
+    garment_type: Annotated[str, Form()],
+    model_gender: Annotated[str, Form()],
+    model_age_range: Annotated[str, Form()],
+    model_body_shape: Annotated[str, Form()],
+    model_race_ethnicity: Annotated[str, Form()],
+    model_pose: Annotated[str, Form()],
+    camera_view_angle: Annotated[str, Form()],
+    camera_distance_meters: Annotated[float, Form()],
+    camera_focal_length_mm: Annotated[float, Form()],
+    camera_aperture_f_number: Annotated[float, Form()],
+    camera_lighting_condition: Annotated[str, Form()],
+    camera_background: Annotated[str, Form()],
+    garment_images: List[UploadFile] = File(..., description="Exactly one image of the garments (e.g., dress /bag/shoes).", min_items=1, max_items=1),
+):
+    """
+    Generate a try-on image based on garment images, model characteristics, and camera settings.
+    """
+    try:
+        garment=GarmentType(garment_type=garment_type)
+        model_attributes = ModelSchema(
+            gender=model_gender,
+            age_range=model_age_range,
+            body_shape=model_body_shape,
+            race_ethnicity=model_race_ethnicity,
+            pose=model_pose
+        )
+        camera_settings = CameraSchema(
+            view_angle=camera_view_angle,
+            distance_meters=camera_distance_meters,
+            focal_length_mm=camera_focal_length_mm,
+            aperture_f_number=camera_aperture_f_number,
+            lighting_condition=camera_lighting_condition,
+            background=camera_background
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid model or camera attributes: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one garment images are required for outfit match.")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=LIFESTYLE_TRYON_PROMPT_TEMPLATE.format(
+            garment_type=garment.garment_type,
+            model_gender=model_attributes.gender,
+            model_age_range=model_attributes.age_range,
+            model_race_ethnicity=model_attributes.race_ethnicity,
+            model_body_shape=model_attributes.body_shape,
+            model_pose=model_attributes.pose,
+            camera_view_angle=camera_settings.view_angle,
+            camera_distance_meters=camera_settings.distance_meters,
+            camera_focal_length_mm=camera_settings.focal_length_mm,
+            camera_aperture_f_number=camera_settings.aperture_f_number,
+            camera_lighting_condition=camera_settings.lighting_condition,
+            camera_background=camera_settings.background
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/two_object_size.py

@@ -0,0 +1,65 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import WALLET_SIZE_COMPARISION_TEMPLATE
+from app.schemas.common_schemas import DimensionSchema
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/walletsize", tags=["wallet-size-comparision"])
+async def size(
+    product_height1: Annotated[float, Form()],
+    product_width1: Annotated[float, Form()],
+    product_length1: Annotated[float, Form()],
+    product_height2: Annotated[float, Form()],
+    product_width2: Annotated[float, Form()],
+    product_length2: Annotated[float, Form()],
+    garment_images: List[UploadFile] = File(..., description="Front-shot images of the two handbags", min_items=2, max_items=2),
+):
+    """
+    Compare two wallets
+    """
+    try:
+       
+        product_dimensions=DimensionSchema(
+            height_cm1=product_height1,
+            width_cm1=product_width1,
+            length_cm1=product_length1,
+            height_cm2=product_height2,
+            width_cm2=product_width2,
+            length_cm2=product_length2
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid dimensions: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 2:
+        raise HTTPException(status_code=400, detail="Exactly two front shot images of wallet")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=WALLET_SIZE_COMPARISION_TEMPLATE.format(
+            height_cm1=product_dimensions.height_cm1,
+            width_cm1=product_dimensions.width_cm1,
+            length_cm1=product_dimensions.length_cm1,
+            height_cm2=product_dimensions.height_cm2,
+            width_cm2=product_dimensions.width_cm2,
+            length_cm2=product_dimensions.length_cm2
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/api/routers/v1/whatfits.py

@@ -0,0 +1,56 @@
+from fastapi import APIRouter, File, UploadFile, HTTPException, Form
+from fastapi.responses import StreamingResponse
+from app.services.image_generation_service import generate_image_from_files_and_prompt
+from app.services.prompt_factory import WHAT_FITS_INSIDE_TEMPLATE 
+from app.schemas.common_schemas import DimensionSchema
+import io
+from typing import List, Annotated
+
+router = APIRouter()
+
+@router.post("/whatfits", tags=["what-fits"])
+async def whatfits(
+    product_height: Annotated[float, Form()],
+    product_width: Annotated[float, Form()],
+    product_length: Annotated[float, Form()],
+    garment_images: List[UploadFile] = File(..., description="Top-shot image of the handbag", min_items=1, max_items=1),
+):
+    """
+    Generate what fits inside the bag
+    """
+    try:
+       
+        product_dimensions=DimensionSchema(
+            height_cm=product_height,
+            width_cm=product_width,
+            length_cm=product_length
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid dimensions: {e}")
+
+    # Validation for exactly two files (FastAPI also handles min_items/max_items for File, but explicit check is fine)
+    if not garment_images or len(garment_images) != 1:
+        raise HTTPException(status_code=400, detail="Exactly one top shot of the image")
+
+    image_bytes_list = []
+    for image_file in garment_images: # No need to check if garment_images is None here
+        if not image_file.content_type.startswith("image/"):
+            raise HTTPException(status_code=400, detail=f"Invalid file type for garment: {image_file.filename}. Must be an image.")
+        content = await image_file.read()
+        image_bytes_list.append((content, image_file.filename)) # Store as tuple (bytes, filename)
+    
+    prompt=WHAT_FITS_INSIDE_TEMPLATE.format(
+            height_cm=product_dimensions.height_cm,
+            width_cm=product_dimensions.width_cm,
+            length_cm=product_dimensions.length_cm
+        )
+
+    generated_image_data = await generate_image_from_files_and_prompt(
+        image_files=image_bytes_list, 
+        prompt=prompt
+    )
+
+    if generated_image_data:
+        return StreamingResponse(io.BytesIO(generated_image_data), media_type="image/png")
+    else:
+        raise HTTPException(status_code=500, detail="Failed to generate image. Check service logs.")

app/core/config.py

@@ -0,0 +1,13 @@
+from pydantic_settings import BaseSettings, SettingsConfigDict
+import os
+
+class Settings(BaseSettings):
+    GEMINI_API_KEY: str
+    IMAGE_GEN_MODEL: str = "gemini-2.0-flash-preview-image-generation"
+
+    # Pydantic-settings will automatically look for a .env file
+    # and load variables from there if they match the field names.
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding='utf-8', extra='ignore')
+
+# Create a single instance of the settings to be used throughout the application
+settings = Settings() 

app/schemas/common_schemas.py

@@ -0,0 +1,45 @@
+from pydantic import BaseModel, Field
+
+class GarmentType(BaseModel):
+    garment_type: str =Field(description="The type of product", enum=["clothing","jewellery and watches","wallet","shoes","handbags"])
+
+class ModelSchema(BaseModel):
+    gender: str = Field(description="The gender of the person in the image", enum=["female"])
+    age_range: str = Field(description="The age range of the person in the image", enum=["teen", "18-25", "26-35", "36-45", "46-55", "56-65", "66+"])
+    body_shape: str = Field(description="The body shape of the person in the image", enum=["rectangle", "pear", "hourglass", "inverted_triangle"])
+    race_ethnicity: str = Field(description="The race and ethnicity of the person in the image", enum=["white", "black", "asian", "latino", "mixed"])
+    pose: str = Field(description="The pose of the person in the image", enum=["standing", "sitting", "lying down", "dancing", "running", "jumping", "walking", "bending", "twisting", "stretching", "flexing", "posing"])
+
+class CameraSchema(BaseModel):
+    view_angle: str = Field(description="The view angle of the camera", enum=["front", "45deg", "left", "right", "back"])
+    distance_meters: float = Field(description="The distance of the camera from the person in the image")
+    focal_length_mm: float = Field(description="The focal length of the camera in millimeters")
+    aperture_f_number: float = Field(description="The aperture f-number of the camera")
+    lighting_condition: str = Field(description="The lighting condition of the image", enum=["studio_softbox", "outdoor_sunny", "indoor_warm", "flat"])
+    background: str = Field(
+                            description="The background of the image",
+                            enum=[
+                                "White",
+                                "Lifestyle",
+                                "Beach Settings",
+                                "Cafe Environment",
+                                "Spring Garden",
+                                "Winter Snow",
+                                "Professional Settings"
+                            ]
+                        )
+
+
+class DimensionSchema(BaseModel):
+    height_cm: float | None = Field(None, description="Height of the product in centimeters.") 
+    width_cm: float | None = Field(None, description="Width of the product in centimeters.")
+    length_cm: float | None = Field(None, description="Length of the product in centimeters.")
+    height_cm1: float | None = Field(None, description="Height of the product in centimeters.") 
+    width_cm1: float | None = Field(None, description="Width of the product in centimeters.")
+    length_cm1: float | None = Field(None, description="Length of the product in centimeters.")
+    height_cm2: float | None = Field(None, description="Height of the product in centimeters.") 
+    width_cm2: float | None = Field(None, description="Width of the product in centimeters.")
+    length_cm2: float | None = Field(None, description="Length of the product in centimeters.")
+
+class SurfaceType(BaseModel):
+    surface_type: str = Field(description="The background surface of the image", enum=["beach mat", "café table" ,"bar table"])