Update app.py

app.py

@@ -1,31 +1,26 @@
-from huggingface_hub import list_models
 import gradio as gr
 from transformers import pipeline
 
-# Fetch timm models from Hugging Face Hub
-timm_models = list_models(filter="timm", sort="downloads", limit=20)  # Fetch top 20 based on downloads
-model_ids = [model.modelId for model in timm_models]
-
-# Initialize a pipeline with a default model
-default_model = model_ids[0]
-pipe = pipeline("image-classification", model=default_model)
-
-# Function for classification
+# Function for image classification
 def classify(image, model_name):
-    pipe.model = model_name  # Update model dynamically
-    results = pipe(image)
-    return {result["label"]: round(result["score"], 2) for result in results}
+    try:
+        pipe = pipeline("image-classification", model=model_name)
+        results = pipe(image)
+        return {result["label"]: round(result["score"], 2) for result in results}
+    except Exception as e:
+        # Handle errors gracefully, e.g., invalid model names
+        return {"Error": str(e)}
 
 # Gradio Interface
 demo = gr.Interface(
     fn=classify,
     inputs=[
         gr.Image(type="pil", label="Upload an Image"),
-        gr.Dropdown(choices=model_ids, label="Select timm Model", value=default_model)
+        gr.Textbox(label="Enter timm Model Name", placeholder="e.g., timm/mobilenetv3_large_100.ra_in1k"),
     ],
     outputs=gr.Label(num_top_classes=3, label="Top Predictions"),
-    title="timm Model Image Classifier",
-    description="Select a timm model and upload an image for classification."
+    title="Custom timm Model Image Classifier",
+    description="Enter a timm model name from Hugging Face, upload an image, and get predictions.",
 )
 
-demo.launch(debug=True)
+demo.launch()