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| import gradio as gr | |
| from transformers import pipeline | |
| # Load Hugging Face models | |
| code_analyzer = pipeline("text-classification", model="microsoft/codebert-base") | |
| nlp_model = pipeline("text2text-generation", model="google/flan-t5-large") | |
| # -------------------------- | |
| # Code Review Function | |
| # -------------------------- | |
| def analyze_code(code): | |
| if not code.strip(): | |
| return "No code provided.", "", "" | |
| result = code_analyzer(code) | |
| return result[0]["label"], "Consider refactoring for better performance", "Medium" | |
| # -------------------------- | |
| # Metadata Validator (Mock) | |
| # -------------------------- | |
| def validate_metadata(metadata): | |
| if not metadata.strip(): | |
| return "No metadata provided.", "", "" | |
| return "Field", "Unused field detected", "Remove it to improve performance" | |
| # -------------------------- | |
| # AI Q&A Generator (No fallback) | |
| # -------------------------- | |
| def process_nlp_query(query): | |
| if not query.strip(): | |
| return "No query provided." | |
| prompt = f"""You are a certified Salesforce Apex expert. Answer this question clearly and accurately:\n\nQuestion: {query}\n\nAnswer:""" | |
| result = nlp_model( | |
| prompt, | |
| max_length=256, | |
| temperature=0.7, | |
| top_k=50, | |
| top_p=0.9, | |
| repetition_penalty=1.3, | |
| do_sample=True | |
| ) | |
| output = result[0]["generated_text"] | |
| if "Answer:" in output: | |
| output = output.split("Answer:")[-1] | |
| lines = output.strip().splitlines() | |
| seen = set() | |
| unique_lines = [line.strip() for line in lines if line.strip() not in seen and not seen.add(line.strip())] | |
| return "\n".join(unique_lines).strip() | |
| # -------------------------- | |
| # Gradio UI | |
| # -------------------------- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🤖 Salesforce AI Code Review & Metadata Assistant") | |
| with gr.Tab("Code Review"): | |
| code_input = gr.Textbox(label="Apex / LWC Code", lines=8) | |
| issue_type = gr.Textbox(label="Issue Type") | |
| suggestion = gr.Textbox(label="AI Suggestion") | |
| severity = gr.Textbox(label="Severity") | |
| code_button = gr.Button("Analyze Code") | |
| code_button.click(analyze_code, inputs=code_input, outputs=[issue_type, suggestion, severity]) | |
| with gr.Tab("Metadata Validation"): | |
| metadata_input = gr.Textbox(label="Metadata XML", lines=8) | |
| mtype = gr.Textbox(label="Type") | |
| issue = gr.Textbox(label="Issue") | |
| recommendation = gr.Textbox(label="Recommendation") | |
| metadata_button = gr.Button("Validate Metadata") | |
| metadata_button.click(validate_metadata, inputs=metadata_input, outputs=[mtype, issue, recommendation]) | |
| with gr.Tab("Ask AI (Natural Language)"): | |
| query_input = gr.Textbox(label="Your question", lines=2, placeholder="e.g. What is a governor limit in Apex?") | |
| response_output = gr.Textbox(label="AI Response", lines=8) | |
| nlp_button = gr.Button("Ask") | |
| nlp_button.click(process_nlp_query, inputs=query_input, outputs=response_output) | |
| demo.launch() | |