Upload 4 files

Dockerfile

@@ -0,0 +1,25 @@
+# Base image using Python 3.9
+FROM python:3.9
+
+# Create a new user to run the app
+RUN useradd -m -u 1000 user
+USER user
+
+# Set environment variables
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the requirements and install dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the application
+COPY --chown=user . /app
+
+# Expose port 7860 for the application
+EXPOSE 7860
+
+# Command to run the FastAPI app using uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,8 +1,8 @@
 ---
-title: Poster
-emoji: 📊
-colorFrom: red
-colorTo: indigo
+title: Fashion Designer
+emoji: 📚
+colorFrom: yellow
+colorTo: red
 sdk: docker
 pinned: false
 ---

main.py

@@ -0,0 +1,177 @@
+import os
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from io import BytesIO
+import base64
+
+# Import the required packages for the language model and prompts
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+
+# Import packages for image processing and Google GenAI client
+from google import genai
+from google.genai import types
+from PIL import Image
+
+# --- Global Initialization ---
+
+# Get API keys from environment variables
+LANGCHAIN_API_KEY = os.getenv("LANGCHAIN_API_KEY")
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+if not LANGCHAIN_API_KEY or not GOOGLE_API_KEY:
+    raise EnvironmentError("API keys for LANGCHAIN_API_KEY and GOOGLE_API_KEY must be set as environment variables.")
+
+def get_llm(api_key: str):
+    """
+    Returns the ChatGroq LLM instance.
+    """
+    llm = ChatGroq(
+        model="meta-llama/llama-4-scout-17b-16e-instruct",
+        temperature=1,
+        max_tokens=1024,
+        api_key=api_key
+    )
+    return llm
+
+# Initialize the LLM instance
+llm = get_llm(LANGCHAIN_API_KEY)
+
+# Define the prompt template for enhancing raw prompts.
+prompt_template = PromptTemplate.from_template('''
+You are a Prompt Enhancement AI Assistant specialized in crafting marketing posters that effectively communicate a brand's message and capture audience attention. Using the provided business description and the user's raw poster concept, generate a detailed, professional prompt optimized for AI-driven poster creation.
+
+Enhance the prompt by including:
+- Visual Style: (e.g., flat design, isometric, photorealistic, illustrative)
+- Color Scheme & Branding: (e.g., vibrant brand colors, pastel accents, monochrome palette)
+- Typography: (e.g., bold sans-serif headline, elegant serif subtext)
+- Composition & Layout: (e.g., focal point placement, use of negative space, layered elements)
+- Imagery Elements: (e.g., product shots, icons, background patterns, textures)
+- Lighting & Mood: (e.g., bright, dramatic shadows, soft ambient glow)
+- Contextual Details: (e.g., logo placement, tagline integration, call-to-action button)
+
+Raw Poster Concept:
+{Raw_Prompt}
+
+Enhanced Poster Prompt:
+
+''')
+
+# Initialize the Google GenAI client.
+client1 = genai.Client(api_key=GOOGLE_API_KEY)
+
+# --- Request Models ---
+
+class EnhancePromptRequest(BaseModel):
+    raw_prompt: str
+
+class GenerateImageRequest(BaseModel):
+    # If both are provided, enhanced_prompt takes priority.
+    raw_prompt: str = None
+    enhanced_prompt: str = None
+
+class UpdateImageRequest(BaseModel):
+    text_instruction: str
+    # Image encoded in base64
+    image_base64: str
+
+# --- FastAPI Initialization ---
+
+app = FastAPI(title="Image Generation & Update API")
+
+# 1. Root endpoint
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Image Generation API!"}
+
+# 2. Enhance Prompt endpoint
+@app.post("/enhance-prompt")
+async def enhance_prompt(request: EnhancePromptRequest):
+    try:
+        # Prepare the prompt using the template.
+        formatted_prompt = prompt_template.invoke({"Raw_Prompt": request.raw_prompt})
+        # Call the LLM to enhance the prompt.
+        response = llm.invoke(formatted_prompt)
+        # Assume the enhanced prompt is in the response.content.
+        enhanced_prompt = response.content
+        return {"raw_prompt": request.raw_prompt, "enhanced_prompt": enhanced_prompt}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Helper function to generate image content using GenAI client.
+def generate_image_from_prompt(image_prompt: str):
+    response = client1.models.generate_content(
+        model="gemini-2.0-flash-exp-image-generation",
+        contents=image_prompt,
+        config=types.GenerateContentConfig(
+            response_modalities=['Text', 'Image']
+        )
+    )
+    # Prepare a dict to hold results.
+    result = {"text": None, "image_base64": None}
+    # Process the response parts.
+    for part in response.candidates[0].content.parts:
+        if part.text is not None:
+            result["text"] = part.text
+        elif part.inline_data is not None:
+            # Open the image from bytes.
+            image = Image.open(BytesIO(part.inline_data.data))
+            # Convert image to bytes.
+            buffered = BytesIO()
+            image.save(buffered, format="PNG")
+            # Encode to base64.
+            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+            result["image_base64"] = img_str
+    return result
+
+# 3. Generate Image endpoint
+@app.post("/generate-poster")
+async def generate_image(request: GenerateImageRequest):
+    try:
+        # Decide which prompt to use.
+        if request.enhanced_prompt:
+            image_prompt = request.enhanced_prompt
+        elif request.raw_prompt:
+            image_prompt = request.raw_prompt
+        else:
+            raise HTTPException(status_code=400, detail="Either raw_prompt or enhanced_prompt must be provided.")
+        
+        result = generate_image_from_prompt(image_prompt)
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 4. Update Image endpoint
+@app.post("/update-poster")
+async def update_image(request: UpdateImageRequest):
+    try:
+        # Decode the incoming base64 image.
+        image_bytes = base64.b64decode(request.image_base64)
+        image = Image.open(BytesIO(image_bytes))
+        
+        # The contents for the update API include the text instruction and the current image.
+        response = client1.models.generate_content(
+            model="gemini-2.0-flash-exp-image-generation",
+            contents=[request.text_instruction, image],
+            config=types.GenerateContentConfig(
+                response_modalities=['Text', 'Image']
+            )
+        )
+        updated_result = {"text": None, "updated_image_base64": None}
+        for part in response.candidates[0].content.parts:
+            if part.text is not None:
+                updated_result["text"] = part.text
+            elif part.inline_data is not None:
+                updated_image = Image.open(BytesIO(part.inline_data.data))
+                buffered = BytesIO()
+                updated_image.save(buffered, format="PNG")
+                updated_img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+                updated_result["updated_image_base64"] = updated_img_str
+        return updated_result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# --- Run the app ---
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -0,0 +1,6 @@
+langchain_groq 
+langchain_core
+google-genai
+fastapi
+uvicorn
+Pillow