add proj

app.py

@@ -0,0 +1,243 @@
+import gradio as gr
+import json
+import os
+import re
+import time
+from typing import Dict, Tuple, Optional, Any
+from generate import load_chat_model
+from pydantic import BaseModel, ConfigDict
+from jinja2 import Template
+from typing_extensions import Literal
+import yaml
+from generate.chat_completion import ChatCompletionModel
+from dotenv import load_dotenv
+import logging
+load_dotenv()
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+logger = logging.getLogger(__name__)
+
+EXAMPLES = [
+    {
+        "title": "Example 1: Basic Improvement",
+        "prompt": "From the following list of Wikipedia article titles, identify which article this sentence came from.\nRespond with just the article title and nothing else.\n\nArticle titles:\n{{titles}}\n\nSentence to classify:\n{{sentence}}",
+        "feedback": "The categories can only be: technology, culture, history, other."
+    }
+    # TODO: Add more examples here
+]
+
+class Prompt(BaseModel):
+    name: str
+    description: Optional[str] = None
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    prompt: str
+    format: Optional[Literal["fstring", "jinja2"]] = "fstring"
+    model_config = ConfigDict(extra='allow')
+
+    @classmethod
+    def from_yaml(cls, yaml_string: str) -> 'Prompt':
+        yaml_data = yaml.safe_load(yaml_string)
+        return cls(**yaml_data)
+
+    def render(self, context: Dict[str, Any] = {}) -> str:            
+        if self.format == "jinja2":
+            return self._render_jinja(context)
+        else:
+            return self._render_fstring(context)
+
+    def _render_fstring(self, context: Dict[str, Any]) -> str:
+        try:
+            return eval(f"f'''{self.prompt}'''", context)
+        except Exception as e:
+            raise ValueError(f"Error rendering f-string: {e}")
+
+    def _render_jinja(self, context: Dict[str, Any]) -> str:
+        try:
+            template = Template(self.prompt)
+            return template.render(**context)
+        except Exception as e:
+            raise ValueError(f"Error rendering Jinja template: {e}")
+        
+    def get_extra_field(self, field_name: str, default: Any = None) -> Any:
+        """Safely get an extra field with a default value if it doesn't exist."""
+        return getattr(self, field_name, default)
+    
+def demo_card_click(e: gr.EventData):
+    index = e._data['component']['index']
+    return DEMO_LIST[index]['description']
+
+def analyze_prompt(target_prompt: str, feedback: str, language: str, model: ChatCompletionModel) -> str:
+    prompt_template = Prompt.from_yaml(open('./prompts/analyze_prompt.yaml', 'r').read())
+    prompt = prompt_template.render({
+        "prompt": target_prompt,
+        "feedback": feedback,
+        "language": language
+    })
+    output = model.generate(prompt, max_tokens=prompt_template.max_tokens, temperature=prompt_template.temperature)
+    output = output.message.content
+    logger.info(f"Prompt Analysis: {output}")
+    report = re.findall(r"<report>(.*?)</report>", output, flags=re.DOTALL)[0]
+    return report
+
+def optimize_prompt(report: str, target_prompt: str, language: str, model: ChatCompletionModel) -> str:
+    prompt_template = Prompt.from_yaml(open('./prompts/optimize_prompt.yaml', 'r').read())
+    prompt = prompt_template.render({
+        "report": report,
+        "prompt": target_prompt,
+        "language": language
+    })
+    output = model.generate(prompt, max_tokens=prompt_template.max_tokens, temperature=prompt_template.temperature)
+    output = output.message.content
+    logger.info(f"Prompt Result: {output}")
+    return output
+
+def process_analysis(target_prompt: str, feedback: str, language: str, model_id: str) -> Tuple[str, int]:
+    """First step: Analyze the prompt and return the report"""
+    model = load_chat_model(model_id)
+    report = analyze_prompt(target_prompt, feedback, language, model)
+    return report
+
+def process_optimization(report: str, target_prompt: str, language: str, model_id: str) -> Tuple[str, int]:
+    """Second step: Generate the optimized prompt based on the analysis"""
+    model = load_chat_model(model_id)
+    optimization = optimize_prompt(report, target_prompt, language, model)
+    return optimization
+
+def save_results(prompt: str, feedback: str, analysis: str, optimization: str, lang: str, model: str):
+    data = {
+        "original_prompt": prompt,
+        "feedback": feedback,
+        "analysis": analysis,
+        "optimized_prompt": optimization,
+        "language": lang,
+        "model": model
+    }
+    
+    # 时间戳文件名
+    timestamp = time.strftime("%Y%m%d%H%M%S")
+    temp_file = f"results_{timestamp}.json"
+    with open(temp_file, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+    return temp_file
+
+def update_api_key(key: str, value: str):
+    """Update the API key environment variable"""
+    if key and value:
+        os.environ[key] = value
+        logger.info(f"API key updated: {key}={value[:5]}...")
+
+def fill_example(example_idx: int):
+    """Auto-fill prompt and feedback with example content"""
+    if 0 <= int(example_idx) < len(EXAMPLES):
+        example = EXAMPLES[int(example_idx)]
+        return example["prompt"], example["feedback"]
+    return "", ""
+
+with gr.Blocks() as demo:    
+    with gr.Row():
+        # Left Column
+        with gr.Column(scale=1):
+            with gr.Row():
+                key_input = gr.Dropdown(
+                    ["DEEPSEEK_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY"],
+                    label="API Key",
+                )
+                value_input = gr.Textbox(
+                    show_label=True,
+                    placeholder="Your API value...",
+                    type="password",
+                    label="API Value"
+                )
+                value_input.change(
+                    fn=update_api_key,
+                    inputs=[key_input, value_input],
+                    outputs=[]
+                )
+            with gr.Row():
+                language = gr.Dropdown(
+                    choices=["中文", "English"],
+                    value="中文",
+                    label="Language/语言"
+                )
+                model = gr.Dropdown(
+                    choices=["deepseek/deepseek-chat", "anthropic/claude-3-5-sonnet-latest", "openai/gpt-4o"],
+                    value="deepseek/deepseek-chat",
+                    label="Model/模型"
+                )
+            
+
+            with gr.Row():            
+                prompt_input = gr.Textbox(
+                    label="Original Prompt/待优化的Prompt",
+                    placeholder="Enter your prompt here...",
+                    lines=5
+                )
+            with gr.Row():
+                feedback_input = gr.Textbox(
+                    label="Feedback/反馈",
+                    placeholder="Enter your feedback here...",
+                    lines=3
+                )
+            # Add example module
+            with gr.Row():
+                example_dropdown = gr.Dropdown(
+                    choices=[(example["title"], i) for i, example in enumerate(EXAMPLES)],
+                    label="Examples/示例",
+                    value=None
+                )
+            # Add example auto-fill event
+            example_dropdown.change(
+                fn=fill_example,
+                inputs=[example_dropdown],
+                outputs=[prompt_input, feedback_input]
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button("Optimize")
+                download_btn = gr.Button("Download")
+
+        # Right Column
+        with gr.Column(scale=1):
+            analysis_output = gr.Textbox(
+                label="Prompt Analysis",
+                lines=10,
+            )
+            optimization_output = gr.Textbox(
+                label="Optimized Prompt",
+                lines=20,
+            )
+            copy_btn = gr.Button("Copy to Clipboard")
+
+    # First submit handler for analysis
+    submit_btn.click(
+        fn=process_analysis,
+        inputs=[prompt_input, feedback_input, language, model],
+        outputs=analysis_output
+    ).then(
+        fn=process_optimization,
+        inputs=[analysis_output, prompt_input, language, model],
+        outputs=optimization_output
+    )
+
+    # Copy button handler
+    copy_btn.click(
+        fn=None,
+        inputs=[optimization_output],
+        outputs=None,
+        js="(text) => {navigator.clipboard.writeText(text); return null;}"
+    )
+
+    download_btn.click(
+        fn=save_results,
+        inputs=[prompt_input, feedback_input, analysis_output, optimization_output, language, model],
+        outputs=[
+            gr.File(label="Download Results")
+        ]
+    )
+
+
+if __name__ == "__main__":
+    demo.launch(debug=True)

requirements.txt

@@ -0,0 +1,2 @@
+jinja2
+generate-core