Adding actual fields for models.

app.py

@@ -2,20 +2,166 @@ import gradio as gr
 from huggingface_hub import HfApi
 import os
 
+import datetime
+
+OWNER = "EnergyStarAI"
+COMPUTE_SPACE = f"{OWNER}/launch-computation-example"
+REQUESTS_DATASET_PATH = f"{OWNER}/requests_debug"
+
 TOKEN = os.environ.get("DEBUG")
 API = HfApi(token=TOKEN)
 
+
 def update(name):
-    API.restart_space("EnergyStarAI/launch-computation-example")  
-    return f"Okay! EnergyStarAI/launch-computation-example should be running now!"
+    API.restart_space(COMPUTE_SPACE)  
+    return f"Okay! {COMPUTE_SPACE} should be running now!"
+
+
+def get_model_size(model_info: ModelInfo, precision: str):
+    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
+    try:
+        model_size = round(model_info.safetensors["total"] / 1e9, 3)
+    except (AttributeError, TypeError):
+        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
+    
+    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
+    model_size = size_factor * model_size
+    return model_size
+
+
+def add_new_eval(
+    repo_id: str,
+    base_model: str,
+    revision: str,
+    precision: str,
+    weight_type: str,
+    model_type: str,
+):
+    model_owner = repo_id.split("/")[0]
+    model_name = repo_id.split("/")[1]
+    precision = precision.split(" ")[0]
+
+    out_dir = f"{EVAL_REQUESTS_PATH}/{model_owner}"
+    print("Making Dataset directory to output results at %s" % out_dir)
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = f"{EVAL_REQUESTS_PATH}/{model_owner}/{model_name}_eval_request_{precision}_{weight_type}.json"
+    
+    current_time = datetime.now(datetime.timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+    #if model_type is None or model_type == "":
+    #    return styled_error("Please select a model type.")
+
+    # Does the model actually exist?
+    #if revision == "":
+    revision = "main"
+
+    # Is the model on the hub?
+    #if weight_type in ["Delta", "Adapter"]:
+    #    base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
+    #    if not base_model_on_hub:
+    #        return styled_error(f'Base model "{base_model}" {error}')
+
+    #if not weight_type == "Adapter":
+    #    model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
+    #    if not model_on_hub:
+    #        return styled_error(f'Model "{model}" {error}')
+
+    # Is the model info correctly filled?
+    try:
+        model_info = API.model_info(repo_id=repo_id, revision=revision)
+    except Exception:
+        print("Could not find information for model %s at revision %s" % (model, revision))
+        return
+    #    return styled_error("Could not get your model information. Please fill it up properly.")
+
+    model_size = get_model_size(model_info=model_info, precision=precision)
+
+    # Were the model card and license filled?
+    #try:
+    #    license = model_info.cardData["license"]
+    #except Exception:
+    #    return styled_error("Please select a license for your model")
+
+    #modelcard_OK, error_msg = check_model_card(model)
+    #if not modelcard_OK:
+    #    return styled_error(error_msg)
+
+    # Seems good, creating the eval
+    print("Adding request")
+
+    request_entry = {
+        "model": repo_id,
+        "base_model": base_model,
+        "revision": revision,
+        "precision": precision,
+        "weight_type": weight_type,
+        "status": "PENDING",
+        "submitted_time": current_time,
+        "model_type": model_type,
+        "likes": model_info.likes,
+        "params": model_size}
+        #"license": license,
+        #"private": False,
+    #}
+
+    # Check for duplicate submission
+    #if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
+    #    return styled_warning("This model has been already submitted.")
+
+    print("Writing out request file to %s" % out_path)
+    with open(out_path, "w") as f:
+        f.write(json.dumps(eval_entry))
+
+
+        
 
 with gr.Blocks() as demo:
     gr.Markdown("This is a super basic example 'frontend'. Start typing below and then click **Run** to launch the job.")
     gr.Markdown("The job will be launched at [EnergyStarAI/launch-computation-example](https://huggingface.co/spaces/EnergyStarAI/launch-computation-example)")
     with gr.Row():
-        inp = gr.Textbox(placeholder="Textfields/dropdowns/etc for different options would go here.")
-        out = gr.Markdown()
-    btn = gr.Button("Run")
-    btn.click(fn=update, inputs=inp, outputs=out)
+            gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+            with gr.Row():
+                with gr.Column():
+                    model_name_textbox = gr.Textbox(label="Model name")
+                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                    model_type = gr.Dropdown(
+                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+                        label="Model type",
+                        multiselect=False,
+                        value=None,
+                        interactive=True,
+                    )
+
+                with gr.Column():
+                    precision = gr.Dropdown(
+                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
+                        label="Precision",
+                        multiselect=False,
+                        value="float16",
+                        interactive=True,
+                    )
+                    weight_type = gr.Dropdown(
+                        choices=[i.value.name for i in WeightType],
+                        label="Weights type",
+                        multiselect=False,
+                        value="Original",
+                        interactive=True,
+                    )
+                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
 
+            submit_button = gr.Button("Run Analysis")
+            submission_result = gr.Markdown()
+            submit_button.click(
+                fn=add_new_eval,
+                inputs=[
+                    model_name_textbox,
+                    base_model_name_textbox,
+                    revision_name_textbox,
+                    precision,
+                    weight_type,
+                    model_type,
+                ],
+                outputs=submission_result,
+            )
+    
 demo.launch()