create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from datasets import load_dataset
+
+# Load the dataset from Hugging Face
+dataset = load_dataset("princeton-nlp/SWE-bench")
+
+# Define a function to display a sample from the dataset
+
+def get_result(id,name):
+    if name == "":
+        return get_sample(id)
+    return get_sample_filter(name)
+
+def get_sample(sample_id):
+    try:
+        # Get the sample from the dataset
+        sample = dataset['test'][int(sample_id)]
+        return sample
+    except:
+        return {"error": "Invalid sample ID"}
+
+def get_sample_filter(repo_name):
+        try:
+            # Find the sample with the specified repository name in 'train' dataset
+            result = [] 
+            for sample in dataset['test']:
+                if repo_name in sample["repo"]:
+
+                    issue_id=sample["instance_id"].split("-")[1]
+                    repo=sample["repo"]
+                    issue_url=f"https://{sample['repo']}/issues/{issue_id}"
+                    base_commit=sample["base_commit"]
+                    result.append({"repo":repo,"base_commit":base_commit,"issue_url":issue_url})  # Return the found sample as a response
+
+            return result 
+        except Exception as e:
+            return {"error": f"Invalid repository name or error occurred: {str(e)}"}
+
+
+# Create the Gradio interface
+#repo_name = gr.Slider(value=0, min=0, max=len(dataset["test"]), step=1, label='Repository Name') 
+#repo_name = gr.Slider(value=0, min=0, max=len(dataset["test"]), step=1, label='Repository Name') 
+slider = gr.Slider(label="Select ID", minimum=0, maximum=2248, step=1, value=0)
+input_box=gr.Textbox(lines=1, placeholder="Enter sample ID (integer)") 
+
+iface = gr.Interface(
+    fn=get_result,
+    #inputs=gr.Textbox(lines=1, placeholder="Enter sample ID (integer)"), 
+    inputs=[slider,input_box],
+    outputs=gr.JSON(),
+    title="SWE-bench Dataset Viewer",
+    description="Enter a sample ID to view the corresponding data from the SWE-bench dataset."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch(share=True)