evaluate to cli and hf repo to hf env var

app.py

@@ -14,6 +14,9 @@ from qwen_classifier.config import HF_REPO
 from pydantic import BaseModel
 
 app = FastAPI(title="Qwen Classifier")
+hf_repo = os.getenv("HF_REPO")
+if not hf_repo:
+    hf_repo = HF_REPO
 
 # Add this endpoint
 @app.get("/", response_class=HTMLResponse)
@@ -31,7 +34,7 @@ def home():
                 <li><strong>POST /evaluate</strong> - Evaluate batch text prediction from zip file</li>
                 <li><strong>GET /health</strong> - Check API status</li>
             </ul>
-            <p>Try it: <code>curl -X POST <SPACE_URL>/predict -H "Content-Type: application/json" -d '{"text":"your text"}'</code></p>
+            <p>Try it: <code>curl -X POST https://keivanr-qwen-classifier-demo.hf.space/predict -H "Content-Type: application/json" -d '{"text":"your text"}'</code></p>
         </body>
     </html>
     """
@@ -50,7 +53,7 @@ async def load_model():
     
     # Load model (will cache in /home/user/.cache/huggingface)
     app.state.model = QwenClassifier.from_pretrained(
-        HF_REPO,
+        hf_repo,
     )
     print("Model loaded successfully!")
 
@@ -59,13 +62,16 @@ async def load_model():
 class PredictionRequest(BaseModel):
     text: str  # ← Enforces that 'text' must be a non-empty string
 
+class EvaluationRequest(BaseModel):
+    file_path: str  # ← Enforces that 'text' must be a non-empty string
+
 @app.post("/predict")
 async def predict(request: PredictionRequest):  # ← Validates input automatically
-    return predict_single(request.text, HF_REPO, backend="local")
+    return predict_single(request.text, hf_repo, backend="local")
 
 @app.post("/evaluate")
-async def evaluate(request: PredictionRequest):  # ← Validates input automatically
-    return evaluate_batch(request.text, HF_REPO, backend="local")
+async def evaluate(request: EvaluationRequest):  # ← Validates input automatically
+    return evaluate_batch(request.file_path, hf_repo, backend="local")
 
 @app.get("/health")
 def health_check():

qwen_classifier/cli.py

@@ -1,5 +1,6 @@
 import click
 from .predict import predict_single
+from .evaluate import evaluate_batch
 import warnings
 from transformers import logging as hf_logging
 from .config import HF_REPO
@@ -42,4 +43,26 @@ def predict(ctx, text, hf_repo, backend, hf_token):
         backend=backend, 
         hf_token=hf_token
     )
+    click.echo(f"Prediction results: {results}")
+
+@cli.command()
+@click.argument('file_path')
+@click.option('--hf-token', envvar="HF_TOKEN", help="HF API token (or set HF_TOKEN env variable)")
+@click.option('--hf-repo', default=HF_REPO, help="Hugging Face model repo")
+@click.option('--backend', 
+              type=click.Choice(['local', 'hf'], case_sensitive=False),
+              default='local',
+              help="Inference backend: 'local' (your machine) or 'hf' (Hugging Face API)")
+@click.pass_context
+def evaluate(ctx, file_path, hf_repo, backend, hf_token):
+    """Make prediction on a single text"""
+    if ctx.obj['DEBUG']:
+        click.echo("Debug mode enabled - showing all warnings")
+    
+    results = evaluate_batch(
+        file_path,
+        hf_repo, 
+        backend=backend, 
+        hf_token=hf_token
+    )
     click.echo(f"Prediction results: {results}")

qwen_classifier/evaluate.py

@@ -63,11 +63,11 @@ def preprocessing(df):
 
 
 
-def evaluate_batch(text, hf_repo, backend="local", hf_token=None):
+def evaluate_batch(file_path, hf_repo, backend="local", hf_token=None):
     if backend == "local":
-        return _evaluate_local(text, hf_repo)
+        return _evaluate_local(file_path, hf_repo)
     elif backend == "hf":
-        return _evaluate_hf_api(text, hf_token)
+        return _evaluate_hf_api(file_path, hf_token)
     else:
         raise ValueError(f"Unknown backend: {backend}")
 
@@ -136,11 +136,11 @@ def _evaluate_local(test_data_path, hf_repo):
     return metrics, report
 
 
-def _evaluate_hf_api(text, hf_token=None):
+def _evaluate_hf_api(file_path, hf_token=None):
     try:
         response = requests.post(
             f"{SPACE_URL}/evaluate",
-            json={"text": text},  # This matches the Pydantic model
+            json={"file_path": file_path},  # This matches the Pydantic model
             headers={
                 "Authorization": f"Bearer {hf_token}",
                 "Content-Type": "application/json"