Upload app.py

app.py

@@ -1,106 +1,27 @@
-from fastapi import APIRouter
-from datetime import datetime
-from datasets import load_dataset
-from sklearn.metrics import accuracy_score
-from sklearn.linear_model import LogisticRegression
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.model_selection import train_test_split
-
-from evaluation import TextEvaluationRequest
-from emissions import tracker, clean_emissions_data, get_space_info
-
-router = APIRouter()
-
-DESCRIPTION = "Logistic Regression"
-ROUTE = "/text"
-
-@router.post(ROUTE, tags=["Text Task"], 
-             description=DESCRIPTION)
-async def evaluate_text(request: TextEvaluationRequest):
-    """
-    Evaluate text classification for climate disinformation detection.
-    
-    Current Model: Logistic regression
-    - Used as a baseline for comparison
-    """
-    # Get space info
-    username, space_url = get_space_info()
-
-
-
-
-    # Define the label mapping
-    LABEL_MAPPING = {
-        "0_not_relevant": 0,
-        "1_not_happening": 1,
-        "2_not_human": 2,
-        "3_not_bad": 3,
-        "4_solutions_harmful_unnecessary": 4,
-        "5_science_unreliable": 5,
-        "6_proponents_biased": 6,
-        "7_fossil_fuels_needed": 7
-    }
-
-    # Load and prepare the dataset
-    dataset = load_dataset(request.dataset_name)
-
-    # Convert string labels to integers
-    dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
-
-    # Split dataset
-    #train_test = dataset.train_test_split(test_size=.33, seed=42)
-    train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
-    test_dataset = train_test["test"]
-
-    tfidf_vect = TfidfVectorizer(stop_words = 'english')
-    
-    tfidf_train = tfidf_vect.fit_transform(train_dataset['quote'])
-    tfidf_test = tfidf_vect.transform(test_dataset['quote'])
-
-
-    # Start tracking emissions
-    tracker.start()
-    tracker.start_task("inference")
-
-    #--------------------------------------------------------------------------------------------
-    # YOUR MODEL INFERENCE CODE HERE
-    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
-    #--------------------------------------------------------------------------------------------   
-    
-    # Make random predictions (placeholder for actual model inference)
-    true_labels = test_dataset["label"]
-
-    LR = LogisticRegression(class_weight='balanced', max_iter=20, random_state=1234,
-                   solver='liblinear')
-    LR.fit(pd.DataFrame.sparse.from_spmatrix(tfidf_train), pd.DataFrame(y_train_v))
-    predictions=LR.predict(pd.DataFrame.sparse.from_spmatrix(tfidf_test))
-    #--------------------------------------------------------------------------------------------
-    # YOUR MODEL INFERENCE STOPS HERE
-    #--------------------------------------------------------------------------------------------   
-
-    
-    # Stop tracking emissions
-    emissions_data = tracker.stop_task()
-    
-    # Calculate accuracy
-    accuracy = accuracy_score(true_labels, predictions)
-    
-    # Prepare results dictionary
-    results = {
-        "username": username,
-        "space_url": space_url,
-        "submission_timestamp": datetime.now().isoformat(),
-        "model_description": DESCRIPTION,
-        "accuracy": float(accuracy),
-        "energy_consumed_wh": emissions_data.energy_consumed * 1000,
-        "emissions_gco2eq": emissions_data.emissions * 1000,
-        "emissions_data": clean_emissions_data(emissions_data),
-        "api_route": ROUTE,
-        "dataset_config": {
-            "dataset_name": request.dataset_name,
-            "test_size": request.test_size,
-            "test_seed": request.test_seed
+from fastapi import FastAPI
+from dotenv import load_dotenv
+from tasks import text, image, audio
+
+# Load environment variables
+load_dotenv()
+
+app = FastAPI(
+    title="Frugal AI Challenge API",
+    description="API for the Frugal AI Challenge evaluation endpoints"
+)
+
+# Include all routers
+app.include_router(text.router)
+app.include_router(image.router)
+app.include_router(audio.router)
+
+@app.get("/")
+async def root():
+    return {
+        "message": "Welcome to the Frugal AI Challenge API",
+        "endpoints": {
+            "text": "/text - Text classification task",
+            "image": "/image - Image classification task (coming soon)",
+            "audio": "/audio - Audio classification task (coming soon)"
         }
-    }
-    
-    return results 
+    }