fixed merge issue

app.py

@@ -307,72 +307,73 @@ with st.form(key="form"):
     selected_models = st.multiselect("Select the models you wish to evaluate", compatible_models)
     submit_button = st.form_submit_button("Make submission")
     if submit_button:
-        project_id = str(uuid.uuid4())[:3]
-        payload = {
-            "username": AUTOTRAIN_USERNAME,
-            "proj_name": f"my-eval-project-{project_id}",
-            "task": TASK_TO_ID[selected_task],
-            "config": {
-                "language": "en",
-                "max_models": 5,
-                "instance": {
-                    "provider": "aws",
-                    "instance_type": "ml.g4dn.4xlarge",
-                    "max_runtime_seconds": 172800,
-                    "num_instances": 1,
-                    "disk_size_gb": 150,
-                },
-                "evaluation": {
-                    "metrics": selected_metrics,
-                    "models": selected_models,
-                },
-            },
-        }
-        print(f"Payload: {payload}")
-        project_json_resp = http_post(
-            path="/projects/create",
-            payload=payload,
-            token=HF_TOKEN,
-            domain=AUTOTRAIN_BACKEND_API,
-        ).json()
-        print(project_json_resp)
-
-        if project_json_resp["created"]:
+        if len(selected_models) > 0:
+            project_id = str(uuid.uuid4())[:3]
             payload = {
-                "split": 4,  # use "auto" split choice in AutoTrain
-                "col_mapping": col_mapping,
-                "load_config": {"max_size_bytes": 0, "shuffle": False},
+                "username": AUTOTRAIN_USERNAME,
+                "proj_name": f"my-eval-project-{project_id}",
+                "task": TASK_TO_ID[selected_task],
+                "config": {
+                    "language": "en",
+                    "max_models": 5,
+                    "instance": {
+                        "provider": "aws",
+                        "instance_type": "ml.g4dn.4xlarge",
+                        "max_runtime_seconds": 172800,
+                        "num_instances": 1,
+                        "disk_size_gb": 150,
+                    },
+                    "evaluation": {
+                        "metrics": selected_metrics,
+                        "models": selected_models,
+                    },
+                },
             }
-            data_json_resp = http_post(
-                path=f"/projects/{project_json_resp['id']}/data/{selected_dataset}",
+            print(f"Payload: {payload}")
+            project_json_resp = http_post(
+                path="/projects/create",
                 payload=payload,
                 token=HF_TOKEN,
                 domain=AUTOTRAIN_BACKEND_API,
-                params={
-                    "type": "dataset",
-                    "config_name": selected_config,
-                    "split_name": selected_split,
-                },
             ).json()
-            print(data_json_resp)
-            if data_json_resp["download_status"] == 1:
-                train_json_resp = http_get(
-                    path=f"/projects/{project_json_resp['id']}/data/start_process",
+            print(project_json_resp)
+
+            if project_json_resp["created"]:
+                payload = {
+                    "split": 4,  # use "auto" split choice in AutoTrain
+                    "col_mapping": col_mapping,
+                    "load_config": {"max_size_bytes": 0, "shuffle": False},
+                }
+                data_json_resp = http_post(
+                    path=f"/projects/{project_json_resp['id']}/data/{selected_dataset}",
+                    payload=payload,
                     token=HF_TOKEN,
                     domain=AUTOTRAIN_BACKEND_API,
+                    params={
+                        "type": "dataset",
+                        "config_name": selected_config,
+                        "split_name": selected_split,
+                    },
                 ).json()
-                print(train_json_resp)
-                if train_json_resp["success"]:
-                    st.success(f"✅ Successfully submitted evaluation job with project ID {project_id}")
-                    st.markdown(
-                        f"""
-                    Evaluation takes appoximately 1 hour to complete, so grab a ☕ or 🍵 while you wait:
-
-                    📊 Click [here](https://hf.co/spaces/autoevaluate/leaderboards?dataset={selected_dataset}) \
-                        to view the results from your submission
-                    """
-                    )
-                else:
-                    st.error("🙈 Oh noes, there was an error submitting your evaluation job!")
-    else:
-        st.warning("⚠️ No models were selected for evaluation!")
+                print(data_json_resp)
+                if data_json_resp["download_status"] == 1:
+                    train_json_resp = http_get(
+                        path=f"/projects/{project_json_resp['id']}/data/start_process",
+                        token=HF_TOKEN,
+                        domain=AUTOTRAIN_BACKEND_API,
+                    ).json()
+                    print(train_json_resp)
+                    if train_json_resp["success"]:
+                        st.success(f"✅ Successfully submitted evaluation job with project ID {project_id}")
+                        st.markdown(
+                            f"""
+                        Evaluation takes appoximately 1 hour to complete, so grab a ☕ or 🍵 while you wait:
+
+                        📊 Click [here](https://hf.co/spaces/autoevaluate/leaderboards?dataset={selected_dataset}) \
+                            to view the results from your submission
+                        """
+                        )
+                    else:
+                        st.error("🙈 Oh noes, there was an error submitting your evaluation job!")
+        else:
+            st.warning("⚠️ No models were selected for evaluation!")