Update app.py

app.py

@@ -1,9 +1,8 @@
 from PIL import Image, ImageDraw, ImageFont
 import tempfile
 import gradio as gr
-from smolagents import CodeAgent, InferenceClientModel, TransformersModel 
+from smolagents import CodeAgent, InferenceClientModel
 from smolagents import DuckDuckGoSearchTool, Tool
-from huggingface_hub import InferenceClient
 from diffusers import DiffusionPipeline
 import torch
 
@@ -64,71 +63,10 @@ def generate_prompts_for_object(object_name):
         "future": f"Show a futuristic version of a {object_name}, by predicting advanced features and futuristic design."
     }
 
-# =========================================================
-# Tool wrapper for m-ric/text-to-image
-# =========================================================
-'''
-class WrappedTextToImageTool(Tool):
-    name = "text_to_image"
-    description = "Generates an image from a text prompt using the m-ric/text-to-image tool."
-    inputs = {
-        "prompt": {
-            "type": "string",
-            "description": "Text prompt to generate an image"
-        }
-    }
-    output_type = "image"
-
-    def __init__(self):
-        self.client = InferenceClient("m-ric/text-to-image")
-
-    def forward(self, prompt):
-        return self.client.text_to_image(prompt)
-
-'''
-
-'''
-class TextToImageTool(Tool):
-    description = "This tool creates an image according to a prompt, which is a text description."
-    name = "image_generator"
-    inputs = {"prompt": {"type": "string", "description": "The image generator prompt. Don't hesitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}}
-    output_type = "image"
-    model_sdxl = "black-forest-labs/FLUX.1-schnell"
-    client = InferenceClient(model_sdxl, provider="replicate")
-
-
-    def forward(self, prompt):
-        return self.client.text_to_image(prompt)
-'''
-'''
-class TextToImageTool(Tool):
-    description = "This tool creates an image according to a prompt. Add details like 'high-res, photorealistic'."
-    name = "image_generator"
-    inputs = {
-        "prompt": {
-            "type": "string",
-            "description": "The image generation prompt"
-        }
-    }
-    output_type = "image"
-
-    def __init__(self):
-        super().__init__()
-        dtype = torch.bfloat16
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"Using device: {device}")
-        self.pipe = DiffusionPipeline.from_pretrained(
-            "aiyouthalliance/Free-Image-Generation-CC0", 
-            torch_dtype=dtype
-        ).to(device)
-
-    def forward(self, prompt):
-        image = self.pipe(prompt).images[0]
-        return image
-'''
+
 image_generation_tool = Tool.from_space(
     "KingNish/Realtime-FLUX",
-    api_name="/infer",  # Optional if there's only one endpoint
+    api_name="/predict",  # Optional if there's only one endpoint
     name="image_generator",
     description="Generate an image from a prompt"
 )
@@ -137,21 +75,9 @@ image_generation_tool = Tool.from_space(
 # =========================================================
 # Tool and Agent Initialization
 # =========================================================
-#image_generation_tool= TextToImageTool()
-#image_generation_tool = WrappedTextToImageTool()
+
 search_tool = DuckDuckGoSearchTool()
-#print('iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii')
 #llm_engine = InferenceClientModel("Qwen/Qwen2.5-72B-Instruct")
-#llm_engine = TransformersModel(
-#    model_id="Qwen/Qwen2.5-72B-Instruct",
-#    device="cuda",
-#    max_new_tokens=5000,
-#)
-
-#from smolagents import LiteLLMModel
-
-#llm_engine  = LiteLLMModel(model_id="Qwen/Qwen2.5-72B-Instruct", temperature=0.2, max_tokens=5000)
-#llm_engine=InferenceClientModel()
 
 llm_engine = InferenceClientModel("Qwen/Qwen2.5-Coder-32B-Instruct")
 agent = CodeAgent(tools=[image_generation_tool, search_tool], model=llm_engine)