Update app.py

app.py

@@ -1,54 +1,35 @@
-import os
-import gradio as gr
-from transformers import AutoModel, AutoTokenizer
+class WebAppTemplate:
+    def __init__(self, template: str, output_path: str):
+        self.template = template
+        self.output_path = output_path
+
+    def generate_code(self, user_input: dict) -> str:
+        # Generate the code based on the user_input and the app template
+        tokenizer, model = load_model(user_input["model_name"], user_input["model_path"])
+        # Use the tokenizer and model to generate the code
+        pass
+
+    def load_model(self, model_name: str, model_path: str) -> Any:
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+        return tokenizer, model
+
+    def main(self, port: int = 8000, debug: bool = False) -> None:
+        # Implement the main function that creates the Gradio interface and launches the app
+        pass
 
-os.environ["GRADIO_SERVER_PORT"] = "8507"
-
-def get_code_generative_models():
-    models_dir = os.path.join(os.getcwd(), "models")
-    if not os.path.exists(models_dir):
-        os.makedirs(models_dir)
-
-    models = []
-    for model_name in os.listdir(models_dir):
-        model_path = os.path.join(models_dir, model_name)
-        if os.path.isdir(model_path):
-            model_info = AutoModel.from_pretrained(model_path)
-            if "config.json" in [f.name for f in model_info.files]:
-                models.append((model_name, model_path))
-    return models
-
-def model_inference(model_name, model_path, input_data):
-    tokenizer = AutoTokenizer.from_pretrained(model_path)
-    model = AutoModel.from_pretrained(model_path)
-    inputs = tokenizer(input_data, return_tensors="pt")
-    outputs = model(**inputs)
-    result = outputs.last_hidden_state[:, 0, :]
-    return result.tolist()
-
-def main():
-    models = get_code_generative_models()
-    with gr.Blocks() as demo:
-        gr.Markdown("### Select Model and Input")
-        with gr.Row():
-            model_name = gr.Dropdown(label="Model", choices=[m[0] for m in models])
-            input_data = gr.Textbox(label="Input")
-
-        model_path = gr.State(None)
-
-        def update_model_path(model_name):
-            model_path.set(next(filter(lambda m: m[0] == model_name, models))[1])
-
-        input_data.change(update_model_path, inputs=model_name, outputs=model_path)
-
-        output = gr.Textbox(label="Output")
+if __name__ == "__main__":
+    # Initialize the app template
+    app_template = WebAppTemplate("template.txt", "output_path")
 
-        def infer(model_name, input_data):
-            return model_inference(model_name, model_path, input_data)
+    # Get user input
+    user_input = get_user_input()
 
-        output.change(fn=infer, inputs=[model_name, input_data], outputs=output)
+    # Generate the code
+    generated_code = app_template.generate_code(user_input)
 
-    interface = demo.launch()
+    # Save the generated code
+    save_generated_code(generated_code)
 
-if __name__ == "__main__":
-    main()
+    # Launch the app
+    app_template.main(port=8000, debug=False)