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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| import requests | |
| # Hugging Face Inference Client | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # GitHub API details | |
| GITHUB_USERNAME = "YOUR_USERNAME" | |
| GITHUB_REPOSITORY = "YOUR_REPOSITORY" | |
| GITHUB_API_TOKEN = os.getenv("GITHUB_API_TOKEN") | |
| # Function to fetch GitHub issues | |
| def fetch_github_issues(): | |
| url = f"https://api.github.com/repos/{GITHUB_USERNAME}/{GITHUB_REPOSITORY}/issues" | |
| headers = { | |
| "Authorization": f"Bearer {GITHUB_API_TOKEN}", | |
| "Accept": "application/vnd.github.v3+json" | |
| } | |
| response = requests.get(url, headers=headers) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| raise Exception(f"Error fetching issues: {response.status_code}") | |
| # Function to analyze issues and provide solutions | |
| def analyze_issues(issue_text, model_name): | |
| nlp = pipeline("text-generation", model=model_name) | |
| result = nlp(issue_text) | |
| return result[0]['generated_text'] | |
| # Function to handle chat responses | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| if message.startswith("/github"): | |
| try: | |
| issues = fetch_github_issues() | |
| issue_list = "\n".join([f"{i+1}. {issue['title']}" for i, issue in enumerate(issues)]) | |
| yield f"Available GitHub Issues:\n{issue_list}\n\nEnter the issue number to analyze:" | |
| except Exception as e: | |
| yield f"Error fetching GitHub issues: {e}" | |
| elif message.isdigit(): | |
| try: | |
| issue_number = int(message) - 1 | |
| issues = fetch_github_issues() | |
| issue = issues[issue_number] | |
| issue_text = issue['title'] + "\n\n" + issue['body'] | |
| resolution = analyze_issues(issue_text, "gpt2") # Default to gpt2 for now | |
| yield f"Resolution for Issue '{issue['title']}':\n{resolution}" | |
| except Exception as e: | |
| yield f"Error analyzing issue: {e}" | |
| else: | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| with gr.Blocks() as demo: | |
| chatbot = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch(share=True, server_name="0.0.0.0", server_port=7860) |