Update app.py

make new updates

app.py

@@ -6,6 +6,9 @@ import yaml
 from tools.final_answer import FinalAnswerTool
 
 from Gradio_UI import GradioUI
+from PIL import Image
+import io
+import base64
 
 # Below is an example of a tool that does nothing. Amaze us with your creativity!
 @tool
@@ -33,6 +36,58 @@ def get_current_time_in_timezone(timezone: str) -> str:
     except Exception as e:
         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:
+    """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).
+    Returns:
+        A base64 encoded string of the generated image.
+    """
+    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()
+        
+        # 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()
+        
+        return f"data:image/png;base64,{img_str}"
+    except requests.exceptions.RequestException as e:
+        return f"API Error: {str(e)}"
+    except Exception as e:
+        return f"Error generating image: {str(e)}"
+
 
 final_answer = FinalAnswerTool()
 model = InferenceClientModel(