Update app.py

app.py

@@ -1,23 +1,121 @@
 from fastapi import FastAPI
+from fastapi.middleware.wsgi import WSGIMiddleware
 from dotenv import load_dotenv
-from tasks import text, image, audio
+from tasks import image
+import gradio as gr
+import requests
+import os
+from huggingface_hub import HfApi
+
 
-# Load environment variables
 load_dotenv()
 
+HF_TOKEN = os.getenv("HF_TOKEN")
+api = HfApi(token=HF_TOKEN)
+
+# FastAPI app
 app = FastAPI(
-    title="Smoke Detector",
+    title="Frugal AI Challenge API",
     description="API for the Frugal AI Challenge evaluation endpoints"
 )
-
-# Include all routers
 app.include_router(image.router)
 
+
 @app.get("/")
 async def root():
     return {
-        "message": "Welcome to the Frugal AI Challenge API",
+        "message": "Wildfire Smoke Detector",
         "endpoints": {
-            "image": "/image - Image classification task",
+            "dataset evaluation": "/image",
+            "single image detection": "/detect-smoke"
         }
-    } 
+    }
+
+# ---------------------
+# Gradio integration
+# ---------------------
+DEFAULT_PARAMS = {
+    "image": {
+        "dataset_name": "pyronear/pyro-sdis",  # Replace with your actual HF dataset
+        "test_size": 0.2,
+        "test_seed": 42
+    }
+}
+
+def evaluate_model(task: str, space_url: str):
+    if "localhost" in space_url:
+        api_url = f"{space_url}/{task}"
+    else:
+        try:
+            # Assume Hugging Face space URL logic
+            info_space = api.space_info(repo_id=space_url)
+            host = info_space.host
+            api_url = f"{host}/{task}"
+        except:
+            return None, None, None, f"Space '{space_url}' not found"
+
+    try:
+        params = DEFAULT_PARAMS[task]
+        response = requests.post(api_url, json=params)
+        if response.status_code != 200:
+            return None, None, None, f"API call failed with status {response.status_code}"
+
+        results = response.json()
+        accuracy = results.get("classification_accuracy", results.get("accuracy", 0))
+        emissions = results.get("emissions_gco2eq", 0)
+        energy = results.get("energy_consumed_wh", 0)
+        return accuracy, emissions, energy, results
+
+    except Exception as e:
+        return None, None, None, str(e)
+
+def evaluate_single_image(image_path, space_url):
+    api_url = f"{space_url}/detect-smoke"
+    with open(image_path, "rb") as f:
+        files = {"file": f}
+        response = requests.post(api_url, files=files)
+
+    if response.status_code != 200:
+        return f"Error: {response.status_code}", None
+
+    result = response.json()
+    msg = "✅ Smoke detected" if result["smoke_detected"] else "❌ No smoke"
+    return msg, result
+
+# Gradio UI
+with gr.Blocks(title="Frugal AI Challenge") as demo:
+    gr.Markdown("# 🌲 Wildfire Smoke Detector")
+
+    with gr.Tab("Evaluate Dataset Model"):
+        text_space_url = gr.Textbox(placeholder="username/your-space", label="API Base URL")
+        text_route = gr.Textbox(value="image", label="Route Name")
+        text_evaluate_btn = gr.Button("Evaluate Model")
+        text_accuracy = gr.Textbox(label="Accuracy")
+        text_emissions = gr.Textbox(label="Emissions (gCO2eq)")
+        text_energy = gr.Textbox(label="Energy (Wh)")
+        text_results_json = gr.JSON(label="Full Results")
+
+        text_evaluate_btn.click(
+            lambda url, route: evaluate_model(route.strip("/"), url),
+            inputs=[text_space_url, text_route],
+            outputs=[text_accuracy, text_emissions, text_energy, text_results_json],
+            concurrency_limit=5,
+            concurrency_id="eval_queue"
+        )
+
+    with gr.Tab("Single Image Detection"):
+        detect_url = gr.Textbox(placeholder="username/your-space",label="API Base URL")
+        image_input = gr.Image(type="filepath", label="Upload Image")
+        detect_button = gr.Button("Detect Smoke")
+        detect_result = gr.Textbox(label="Detection Result")
+        detect_json = gr.JSON(label="Raw Response")
+
+        detect_button.click(
+            evaluate_single_image,
+            inputs=[image_input, detect_url],
+            outputs=[detect_result, detect_json]
+        )
+
+# Mount Gradio to FastAPI
+app.mount("/gradio", WSGIMiddleware(demo))
+