upda

TextGen/ConfigEnv.py

@@ -1,19 +0,0 @@
-"""Config class for handling env variables.
-"""
-from functools import lru_cache
-from pydantic import BaseSettings
-
-class Settings(BaseSettings):
-    APP_ID: str
-    USER_ID: str
-    MODEL_ID: str
-    CLARIFAI_PAT: str
-    MODEL_VERSION_ID: str
-    
-    class Config:
-        env_file = '.env'
-
-@lru_cache()
-def get_settings():
-    return Settings()
-config = get_settings()

TextGen/__init__.py

@@ -1,5 +0,0 @@
-from fastapi import FastAPI
-
-app = FastAPI(title="Deploying FastAPI Apps on Huggingface")
-
-from TextGen import router

TextGen/router.py

@@ -1,53 +0,0 @@
-from pydantic import BaseModel
-
-from .ConfigEnv import config
-from fastapi.middleware.cors import CORSMiddleware
-
-from langchain.llms import Clarifai
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-
-from TextGen import app
-
-class Generate(BaseModel):
-    text:str
-
-def generate_text(prompt: str):
-    if prompt == "":
-        return {"detail": "Please provide a prompt."}
-    else:
-        prompt = PromptTemplate(template=prompt, input_variables=['Prompt'])
-
-        llm = Clarifai(
-            pat = config.CLARIFAI_PAT,
-            user_id = config.USER_ID,
-            app_id = config.APP_ID, 
-            model_id = config.MODEL_ID,
-            model_version_id=config.MODEL_VERSION_ID,
-        )
-
-        llmchain = LLMChain(
-            prompt=prompt,
-            llm=llm
-        )
-
-        llm_response = llmchain.run({"Prompt": prompt})
-        return Generate(text=llm_response)
-
-        
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.get("/", tags=["Home"])
-def api_home():
-    return {'detail': 'Welcome to FastAPI TextGen Tutorial!'}
-
-@app.post("/api/generate", summary="Generate text from prompt", tags=["Generate"], response_model=Generate)
-def inference(input_prompt: str):
-    return generate_text(prompt=input_prompt)

app.py

@@ -1 +1,59 @@
-from TextGen import app
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import FileResponse
+from diffusers import AutoPipelineForImage2Image
+from PIL import Image
+import torch
+import os
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the model
+model_id = "kandinsky-community/kandinsky-2-2-decoder"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = AutoPipelineForImage2Image.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32, use_safetensors=True
+).to(device)
+
+# Create an output directory
+os.makedirs("output_images", exist_ok=True)
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Image-to-Image API!"}
+
+@app.post("/image-to-image/")
+async def image_to_image(
+    prompt: str = Form(...),
+    stren: float = Form(...),
+    negative_prompt: str = Form(...),
+    image: UploadFile = File(...)
+):
+    """
+    Perform image-to-image transformation using a given prompt and input image.
+
+    Args:
+    - prompt (str): Text prompt describing the desired transformation.
+    - image (UploadFile): Input image file.
+
+    Returns:
+    - FileResponse: The transformed image file.
+    """
+    try:
+        # Open and preprocess the input image
+        input_image = Image.open(image.file).convert("RGB")
+        original_size = input_image.size  # Save the original size
+        # Generate the output image using the pipeline
+        generated_image = pipe(prompt=prompt,negative_prompt=negative_prompt ,image=input_image,strength=stren).images[0]
+        resized_image = generated_image.resize(original_size, Image.LANCZOS)
+        # Save the generated image
+        output_path = f"output_images/generated_{image.filename}"
+        resized_image.save(output_path)
+
+        # Return the generated image as a response
+        return FileResponse(output_path, media_type="image/png")
+
+    except Exception as e:
+        return {"error": str(e)}
+

requirements.txt

@@ -1,7 +1,7 @@
-fastapi==0.99.1
+fastapi
 uvicorn
-requests
-pydantic==1.10.12
-langchain
-clarifai
-Pillow
+pillow
+transformers
+diffusers
+torch
+python-multipart