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acecalisto3
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Parent(s):
70e9a38
Update app.py
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app.py
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import subprocess
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import os
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import gradio as gr
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#
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#
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#
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with open(os.path.expanduser("~/miagi/issues/{}/{}.md".format(number, number)), "a") as f:
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f.write("Resolved!\n\n")
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return "Issue resolved"
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# Define the submit resolution function
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def submit_resolution(number):
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# Check if the issue number exists
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if number not in issues_dict:
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return "Issue not found"
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# Document the resolution in a README file
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with open(os.path.expanduser("~/miagi/README.md"), "a") as f:
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f.write(f"Issue {number}: {issues_dict[number]}\n")
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f.write("--------------------\n")
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f.write(open(os.path.expanduser("~/miagi/issues/{}/{}.md".format(number, number))).read())
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f.write("\n\n")
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# Commit and push the changes
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miagi_repo = Repo.init("/tmp/miagi")
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miagi_repo.git.add(".")
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miagi_repo.git.commit(m="Resolved issue {}".format(number))
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miagi_repo.git.push()
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return "Resolution submitted"
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iface.replicate_issue.output.subscribe(issue_description.update)
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iface.resolve_issue.output.subscribe(resolution_notes.update)
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iface.submit_resolution.output.subscribe(lambda _: "Resolution submitted")
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iface.connect(lambda number: replicate_issue(number), "Number")
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iface.connect(lambda _: resolve_issue(issue_number.output.value), "Resolve Issue")
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iface.connect(lambda _: submit_resolution(issue_number.output.value), "Submit Resolution")
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iface.launch()
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import os
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import gradio as gr
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import transformers
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import blackbox_client
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# Set up the Hugging Face Transformers library
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model_name = "bert-base-uncased"
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
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model = transformers.AutoModel.from_pretrained(model_name)
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# Set up the Blackbox.ai API client
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blackbox_client.Client.from_api_key(os.environ["BLACKBOX_API_KEY"])
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# Define the user interface for the app
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def run_model(input_text):
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# Tokenize the input text
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inputs = tokenizer(input_text, return_tensors="pt")
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# Run the model on the inputs
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outputs = model(**inputs)
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# Extract the last hidden state from the model outputs
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last_hidden_states = outputs.last_hidden_state
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# Return the last hidden state as a string
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return last_hidden_states.detach().numpy().tolist()
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iface = gr.Interface(fn=run_model, inputs="text", outputs="text")
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# Define the GitHub bot functions
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def get_issues():
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# Code to get issues from the GitHub repository
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pass
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def fix_issue(issue):
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# Code to fix the issue on the local fork
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pass
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def push_fix():
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# Code to push the fix to the GitHub repository
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pass
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def comment_on_issue(issue, result):
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# Code to comment on the issue with the result
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pass
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# Define the main function to run the app and the bot
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def main():
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# Run the app
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iface.launch()
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# Get the issues from the GitHub repository
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issues = get_issues()
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# Loop through the issues
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for issue in issues:
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# Fix the issue on the local fork
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fixed_issue = fix_issue(issue)
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# Run the model on the fixed issue
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result = run_model(fixed_issue)
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# Push the fix to the GitHub repository
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push_fix()
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# Comment on the issue with the result
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comment_on_issue(issue, result)
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# Run the main function
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if __name__ == "__main__":
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main()
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