new tool added

app.py

@@ -1,3 +1,4 @@
+from huggingface_hub import InferenceClient
 from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
 import datetime
 import requests
@@ -37,54 +38,24 @@ def get_current_time_in_timezone(timezone: str) -> str:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
 @tool
-def generate_image(
-    prompt: str, 
-    negative_prompt: str = "", 
-    steps: int = 20,
-    model_id: str = "stabilityai/stable-diffusion-xl-base-1.0"
-) -> str:
+def generate_image(prompt: str, model: str = "stabilityai/stable-diffusion-xl-base-1.0") -> str:
     """A tool that generates images from text prompts using Hugging Face's Inference API.
-    Automatically uses the HF_TOKEN environment variable for authentication.
-    
     Args:
-        prompt: The text description of the image you want to generate.
-        negative_prompt: Things you don't want to see in the image (optional).
-        steps: Number of denoising steps (default 20, higher is better quality but slower).
-        model_id: The model ID to use (default: stabilityai/stable-diffusion-xl-base-1.0).
+        prompt: The text description of the image to generate.
+        model: The Hugging Face model to use for image generation (default: stabilityai/stable-diffusion-xl-base-1.0).
     Returns:
-        A base64 encoded string of the generated image.
+        URL of the generated image or an error message.
     """
     try:
-        # Get token from environment variables (automatically available on HF Spaces)
-        API_TOKEN = os.environ.get("HF_TOKEN")
-        if not API_TOKEN:
-            return "Error: HF_TOKEN not found in environment variables"
-            
-        API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
-        headers = {"Authorization": f"Bearer {API_TOKEN}"}
-        
-        payload = {
-            "inputs": prompt,
-            "negative_prompt": negative_prompt,
-            "options": {"use_cache": True, "wait_for_model": True},
-            "parameters": {
-                "num_inference_steps": steps,
-                "guidance_scale": 7.5
-            }
-        }
-        
-        response = requests.post(API_URL, headers=headers, json=payload)
-        response.raise_for_status()
+        client = InferenceClient()
+        image = client.text_to_image(prompt, model=model)
         
-        # Convert to base64 for display
-        image = Image.open(io.BytesIO(response.content))
-        buffered = io.BytesIO()
-        image.save(buffered, format="PNG")
-        img_str = base64.b64encode(buffered.getvalue()).decode()
+        # Save the image locally and return the path
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        image_path = f"generated_image_{timestamp}.png"
+        image.save(image_path)
         
-        return f"data:image/png;base64,{img_str}"
-    except requests.exceptions.RequestException as e:
-        return f"API Error: {str(e)}"
+        return f"Image generated successfully! Saved to: {image_path}"
     except Exception as e:
         return f"Error generating image: {str(e)}"
 
@@ -110,7 +81,7 @@ with open("prompts.yaml", 'r') as stream:
     
 agent = CodeAgent(
     model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
+    tools=[final_answer, get_current_time_in_timezone, generate_image], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,