Update app.py

app.py

@@ -1,105 +1,71 @@
-import subprocess
 import os
 import gradio as gr
+import transformers
+import blackbox_client
 
-iface = gr.Blocks()
-
-iface = gr.Blocks()
-
-with iface:
-    gr.Markdown("## Issue Resolution Workshop")
-    issue_number = gr.Number(label="Issue Number")
-    replicate_issue = gr.Button(label="Replicate Issue")
-    resolve_issue = gr.Button(label="Resolve Issue")
-    issue_description = gr.Markdown(label="Issue Description")
-    resolution_notes = gr.Markdown(label="Resolution Notes")
-    resolution_proof = gr.Markdown(label="Resolution Proof")
-    submit_resolution = gr.Button(label="Submit Resolution")
-
-# Set up GitPython
-os.environ["HOME"] = "/tmp"
-repo = Repo.init("/tmp/repo")
-
-# Authenticate with GitHub
-token = os.environ["GITHUB_TOKEN"]
-repo.config_writer().set_value("user", "token", token).release()
-
-# Pull issues from enricoros/big-agi
-issues_url = "https://api.github.com/repos/enricoros/big-agi/issues"
-issues = subprocess.check_output(["curl", "-s", issues_url]).decode().strip().split("\n")
-
-# Loop through the issues and store them in a dictionary
-issues_dict = {}
-for issue in issues:
-    issue = eval(issue)
-    title = issue["title"]
-    number = issue["number"]
-    issues_dict[number] = title
-
-# Define the replicate issue function
-def replicate_issue(number):
-    # Check if the issue number exists
-    if number not in issues_dict:
-        return "Issue not found"
-
-    # Clone your miagi fork
-    miagi_repo = Repo.clone_from("https://github.com/Ig0tU/miagi.git", "/tmp/miagi")
-
-    # Create a new branch for the issue
-    miagi_repo.create_head(f"issue-{number}", miagi_repo.head.reference)
-    miagi_branch = miagi_repo.heads[f"issue-{number}"]
-
-    # Checkout the new branch
-    miagi_branch.checkout()
-
-    # Replicate the issue in the new branch
-    shutil.copytree(os.path.expanduser("~/enricoros/big-agi/issues"), os.path.expanduser("~/miagi/issues"))
-
-    # Update the issue description
-    issue_description.update(issues_dict[number])
-
-    return "Issue replicated"
-
-# Define the resolve issue function
-def resolve_issue(number):
-    # Check if the issue number exists
-    if number not in issues_dict:
-        return "Issue not found"
-
-    # Resolve the issue in the new branch
-    # (Replace this with your own code to resolve the issue)
-    with open(os.path.expanduser("~/miagi/issues/{}/{}.md".format(number, number)), "a") as f:
-        f.write("Resolved!\n\n")
-
-    return "Issue resolved"
-
-# Define the submit resolution function
-def submit_resolution(number):
-    # Check if the issue number exists
-    if number not in issues_dict:
-        return "Issue not found"
-
-    # Document the resolution in a README file
-    with open(os.path.expanduser("~/miagi/README.md"), "a") as f:
-        f.write(f"Issue {number}: {issues_dict[number]}\n")
-        f.write("--------------------\n")
-        f.write(open(os.path.expanduser("~/miagi/issues/{}/{}.md".format(number, number))).read())
-        f.write("\n\n")
-
-    # Commit and push the changes
-    miagi_repo = Repo.init("/tmp/miagi")
-    miagi_repo.git.add(".")
-    miagi_repo.git.commit(m="Resolved issue {}".format(number))
-    miagi_repo.git.push()
-
-    return "Resolution submitted"
-
-iface.replicate_issue.output.subscribe(issue_description.update)
-iface.resolve_issue.output.subscribe(resolution_notes.update)
-iface.submit_resolution.output.subscribe(lambda _: "Resolution submitted")
-
-iface.connect(lambda number: replicate_issue(number), "Number")
-iface.connect(lambda _: resolve_issue(issue_number.output.value), "Resolve Issue")
-iface.connect(lambda _: submit_resolution(issue_number.output.value), "Submit Resolution")
-
-iface.launch()
+# Set up the Hugging Face Transformers library
+model_name = "bert-base-uncased"
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+model = transformers.AutoModel.from_pretrained(model_name)
+
+# Set up the Blackbox.ai API client
+blackbox_client.Client.from_api_key(os.environ["BLACKBOX_API_KEY"])
+
+# Define the user interface for the app
+def run_model(input_text):
+    # Tokenize the input text
+    inputs = tokenizer(input_text, return_tensors="pt")
+
+    # Run the model on the inputs
+    outputs = model(**inputs)
+
+    # Extract the last hidden state from the model outputs
+    last_hidden_states = outputs.last_hidden_state
+
+    # Return the last hidden state as a string
+    return last_hidden_states.detach().numpy().tolist()
+
+iface = gr.Interface(fn=run_model, inputs="text", outputs="text")
+
+# Define the GitHub bot functions
+def get_issues():
+    # Code to get issues from the GitHub repository
+    pass
+
+def fix_issue(issue):
+    # Code to fix the issue on the local fork
+    pass
+
+def push_fix():
+    # Code to push the fix to the GitHub repository
+    pass
+
+def comment_on_issue(issue, result):
+    # Code to comment on the issue with the result
+    pass
+
+# Define the main function to run the app and the bot
+def main():
+    # Run the app
+    iface.launch()
+
+    # Get the issues from the GitHub repository
+    issues = get_issues()
+
+    # Loop through the issues
+    for issue in issues:
+        # Fix the issue on the local fork
+        fixed_issue = fix_issue(issue)
+
+        # Run the model on the fixed issue
+        result = run_model(fixed_issue)
+
+        # Push the fix to the GitHub repository
+        push_fix()
+
+        # Comment on the issue with the result
+        comment_on_issue(issue, result)
+
+# Run the main function
+if __name__ == "__main__":
+    main()