Update app.py

app.py

@@ -1,133 +1,99 @@
 import os
-from typing import Dict, List, Optional
-from dataclasses import dataclass, field
-from transformers import (
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    DataCollatorWithPadding,
-    HfArgumentParser,
-    Trainer,
-    TrainingArguments,
-)
-
-from transformers.models import AutoConfig
-
-@dataclass
-class ModelArguments:
-    model_name: str = field(
-        default="gpt2",
-        metadata={"help": "The name of the pretrained model to use for text generation."}
-    )
-    max_tokens: int = field(
-        default=50,
-        metadata={"help": "The maximum number of tokens to generate in the response."}
-    )
-
-class MockOpenAI:
-    """
-    A mock implementation of OpenAI's API using Hugging Face's pipeline for text generation.
-
-    :param api_key: Your Hugging Face API key, required for authentication.
-    :param base_url: The base URL for the Hugging Face API, defaults to the production URL.
-    :param model_name: The name of the pretrained model to use for text generation, defaults to 'gpt2'.
-    :param max_tokens: The maximum number of tokens to generate in the response, defaults to 50.
-    """
-    def __init__(
-        self,
-        api_key: Optional[str] = None,
-        base_url: Optional[str] = None,
-        model_name: Optional[str] = "gpt2",
-        max_tokens: int = 50,
-    ):
-        self.api_key = api_key or os.environ.get("HUGGING_FACE_API_KEY")
-        self.base_url = base_url or "https://api-inference.huggingface.co/models"
-        self.model_name = model_name
-        self.max_tokens = max_tokens
-        self.config = AutoConfig.from_pretrained(self.model_name)
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-        self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name, config=self.config)
-        self.data_collator = DataCollatorWithPadding(self.tokenizer)
-        self.trainer = Trainer(
-        model=self.model,
-        args=TrainingArguments(
-           output_dir="./",
-           num_train_epochs=1,
-           learning_rate=1e-5,
-           per_device_train_batch_size=16,
-           per_device_eval_batch_size=16,
-           evaluation_strategy="epoch",
-           # Remove this line:
-           # use_seedable_sampler=True, 
-       ),
-   )
-
-    class Chat:
-        if 'toolbox' not in st.session_state:
-    st.session_state['toolbox'] = {}
-        
-        def __init__(self, mock_openai: 'MockOpenAI'):
-            self.mock_openai = mock_openai
-
-        class Completions:
-            def __init__(self, mock_openai: 'MockOpenAI'):
-                self.mock_openai = mock_openai
-
-            def create(
-                self,
-                messages: List[Dict[str, str]],
-                model: Optional[str] = None,
-                max_tokens: int = 50,
-                **kwargs,
-            ):
-                """
-                Generate a text completion based on the given messages.
-
-                :param messages: List of message objects, each containing 'role' and 'content'.
-                :param model: The name of the pretrained model to use for text generation, defaults to 'gpt2'.
-                :param max_tokens: The maximum number of tokens to generate in the response, defaults to 50.
-                :param kwargs: Additional keyword arguments to pass to the pipeline function.
-                :return: A dictionary containing the generated text.
-                """
-                if not self.mock_openai.config.is_decoder:
-                    raise ValueError("This model is not a decoder.")
-
-                model_name = model or self.mock_openai.model_name
-                prompt = " ".join([msg["content"] for msg in messages])
-
-                inputs = self.mock_openai.tokenizer(prompt, padding="max_length", truncation=True)
-                outputs = self.mock_openai.trainer.predict(inputs.to_tensor(pad_to_multiple_of=self.mock_openai.config.max_length))
-                result = self.mock_openai.tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-                if max_tokens is not None and len(result) > max_tokens:
-                    result = result[:max_tokens]
-
-                return result
-
-    @property
-    def chat(self):
-        """
-        Get the Chat class instance with the pretrained model for text generation.
-
-        :return: The Chat class instance.
-        """
-        return self.Chat(self)
-
-# Example usage
+import sys
+import time
+
+import huggingface_hub
+
+from huggingface_hub.commands import HfFolder
+
+import transformers
+
+from transformers import pipeline
+
+import gradio as gr
+
+import tempfile
+
+from huggingface_hub import HfFolder
+
+def main():
+    # Get the user's idea
+    idea = input("What is your idea for an application? ")
+
+    # Generate the code for the application
+    code = gemmacode.generate(idea)
+
+    # Test the code
+    try:
+        transformers.pipeline("text-generation")(code)
+    except Exception as e:
+        print("The code failed to run:", e)
+        return
+
+    # Ensure the functionality of the application
+    try:
+        gr.Interface(fn=transformers.pipeline("text-generation"), inputs=gr.Textbox(), outputs=gr.Textbox()).launch()
+    except Exception as e:
+        print("The application failed to run:", e)
+        return
+
+    # Provide an embedded webapp demo of the user's idea implementation
+    try:
+        hf_folder = HfFolder(path=tempfile.mkdtemp())
+        hf_folder.save(code)
+        hf_folder.push_to_hub(repo_id="acecalisto3/gemmacode-demo", commit_message="Initial commit")
+        print(f"The demo is available at: https://huggingface.co/acecalisto3/gemmacode-demo")
+    except Exception as e:
+        print("The demo failed to launch:", e)
+        return
+
+    # Offer the option to rebuild or deploy
+    while True:
+        choice = input("Do you want to rebuild or deploy the application? (r/d/q) ")
+        if choice == "r":
+            # Rebuild the code
+            code = gemmacode.generate(idea)
+
+            # Test the code
+            try:
+                transformers.pipeline("text-generation")(code)
+            except Exception as e:
+                print("The code failed to run:", e)
+                return
+
+            # Ensure the functionality of the application
+            try:
+                gr.Interface(fn=transformers.pipeline("text-generation"), inputs=gr.Textbox(), outputs=gr.Textbox()).launch()
+            except Exception as e:
+                print("The application failed to run:", e)
+                return
+
+            # Provide an embedded webapp demo of the user's idea implementation
+            try:
+                hf_folder = HfFolder(path=tempfile.mkdtemp())
+                hf_folder.save(code)
+                hf_folder.push_to_hub(repo_id="acecalisto3/gemmacode-demo", commit_message="Initial commit")
+                print(f"The demo is available at: https://huggingface.co/acecalisto3/gemmacode-demo")
+            except Exception as e:
+                print("The demo failed to launch:", e)
+                return
+        elif choice == "d":
+            # Deploy the application
+            try:
+                api_token = os.environ["HF_TOKEN"]
+                hub = huggingface_hub.HfApi(api_token=api_token)
+                hub.create_repo(name="my-app", organization="my-org")
+                hf_folder = HfFolder(path=tempfile.mkdtemp())
+                hf_folder.save(code)
+                hf_folder.push_to_hub(repo_id="my-org/my-app", commit_message="Initial commit")
+                print("The application has been deployed to: https://huggingface.co/my-org/my-app")
+            except Exception as e:
+                print("The application failed to deploy:", e)
+                return
+        elif choice == "q":
+            break
+        else:
+            print("Invalid choice")
+
 if __name__ == "__main__":
-    parser = HfArgumentParser((ModelArguments,))
-    model_args = parser.parse_args_into_dataclasses()[0]
-    client = MockOpenAI(model_name=model_args.model_name, max_tokens=model_args.max_tokens)
-    chat_completion = client.chat.Completions().create(
-        messages=[
-            {
-                "role": "system",
-                "content": "You are a helpful assistant.",
-            },
-            {
-                "role": "user",
-                "content": "What is deep learning?",
-            }
-        ]
-    )
-
-    print(chat_completion)
+    main()