Update tasks/text.py

tasks/text.py

@@ -7,9 +7,12 @@ import random
 from .utils.evaluation import TextEvaluationRequest
 from .utils.emissions import tracker, clean_emissions_data, get_space_info
 
+from huggingface_hub import InferenceClient
+import json
+
 router = APIRouter()
 
-DESCRIPTION = "Random Baseline"
+DESCRIPTION = "Modified small RoBERTa checkpoint that focuses on emotions"
 ROUTE = "/text"
 
 @router.post(ROUTE, tags=["Text Task"], 
@@ -55,6 +58,43 @@ async def evaluate_text(request: TextEvaluationRequest):
     # 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.
     #--------------------------------------------------------------------------------------------   
+
+
+    repo_id = "nhankins/frugal_ai_submission"
+
+
+    llm_client = InferenceClient(
+        
+
+        model=repo_id,
+
+        timeout=120,
+    )
+
+
+    def call_llm(inference_client: InferenceClient, prompt: str):
+
+        response = inference_client.post(
+
+            json={
+
+                "inputs": prompt,
+
+                "parameters": {"max_new_tokens": 200},
+
+                "task": "text-classification",
+
+            },
+
+        )
+
+    return json.loads(response.decode())[0]["generated_label"]
+
+
+
+    response=call_llm(llm_client, "climate disinformation here")
+
+    print (response)
     
     # Make random predictions (placeholder for actual model inference)
     true_labels = test_dataset["label"]