Update app.py

app.py

@@ -1,48 +1,40 @@
 import gradio as gr
 import os
 import requests
+import json
+from huggingface_hub import HfApi
+import huggingface_hub
 
-TITLE = "Andy Ai"
-EXAMPLE_INPUT = "Your default example input"
+# Constants for Zephyr-7b-Beta model
+zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/"
+HF_TOKEN = os.getenv("HF_TOKEN")
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
 
+# Constants for Gradio UI
 constants = """
 SYSTEM_PROMPT = "{}"
 TITLE = "{}"
 EXAMPLE_INPUT = "{}"
 """
 
-
-zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/"
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
-
+# Function to build input prompt for the model
 def build_input_prompt(message, chatbot, system_prompt):
-    """
-    Constructs the input prompt string from the chatbot interactions and the current message.
-    """
-    input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
+    input_prompt = "\n" + system_prompt + "</s>\n\n"
     for interaction in chatbot:
-        input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n"
-
-    input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
+        input_prompt = input_prompt + str(interaction[0]) + "</s>\n\n" + str(interaction[1]) + "\n</s>\n\n"
+    input_prompt = input_prompt + str(message) + "</s>\n"
     return input_prompt
 
-
+# Function to send a POST request to Zephyr-7b-Beta model
 def post_request_beta(payload):
-    """
-    Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response.
-    """
     response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload)
-    response.raise_for_status()  # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
+    response.raise_for_status()
     return response.json()
 
-
+# Function to get model predictions
 def predict_beta(message, chatbot=[], system_prompt=""):
     input_prompt = build_input_prompt(message, chatbot, system_prompt)
-    data = {
-        "inputs": input_prompt
-    }
+    data = {"inputs": input_prompt}
 
     try:
         response_data = post_request_beta(data)
@@ -63,21 +55,84 @@ def predict_beta(message, chatbot=[], system_prompt=""):
         error_msg = f"Failed to decode response as JSON: {str(e)}"
         raise gr.Error(error_msg)
 
-def test_preview_chatbot(message, history):
-    response = predict_beta(message, history, SYSTEM_PROMPT)
-    text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
-    response = response[text_start:]
-    return response
-
-
-welcome_preview_message = f"""
-Welcome to **{TITLE}**! Say something like: 
-"{EXAMPLE_INPUT}"
-"""
-
-chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)])
-textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
-
-demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)
-
-demo.launch(share=True)
+# Function to extract title, system prompt, and example input from model response
+def extract_title_prompt_example(text, title, system_prompt, example_input):
+    try:
+        text_start = text.rfind("", ) + len("")
+        text = text[text_start:]
+    except ValueError:
+        pass
+    try:
+        title_start = text.lower().rfind("title:") + len("title:")    
+        prompt_start = text.lower().rfind("system prompt:")
+        title = text[title_start:prompt_start].strip()
+    except ValueError:
+        pass
+    try:
+        prompt_start = text.lower().rfind("system prompt:") + len("system prompt:")
+        example_start = text.lower().rfind("example input:")
+        system_prompt = text[prompt_start:example_start].strip()
+    except ValueError:
+        pass
+    try:
+        example_start = text.lower().rfind("example input:") + len("example input:")
+        example_input = text[example_start:].strip()
+        example_input = example_input[:example_input.index("\n")]
+    except ValueError:
+        pass
+    return text, title, system_prompt, example_input
+
+# Function to make an Open GPT
+def make_open_gpt(message, history, current_title, current_system_prompt, current_example_input):
+    response = predict_beta(message, history, current_system_prompt)
+    response, title, system_prompt, example_input = extract_title_prompt_example(response, current_title, current_system_prompt, current_example_input)
+    return "", history + [(message, response)], title, system_prompt, example_input, [(None, welcome_preview_message.format(title, example_input))], example_input, gr.Column(visible=True), gr.Group(visible=True)
+
+# Function to set title and example input for preview
+def set_title_example(title, example):
+    return [(None, welcome_preview_message.format(title, example))], example, gr.Column(visible=True), gr.Group(visible=True)
+
+# Function to publish the GPT to Hugging Face Spaces
+def publish(textbox_system_prompt, textbox_title, textbox_example, textbox_token):
+    source_file = 'app_template.py'
+    destination_file = 'app.py'
+    constants_formatted = constants.format(textbox_system_prompt, textbox_title, textbox_example)
+    with open(source_file, 'r') as file:
+        original_content = file.read()
+    with open(destination_file, 'w') as file:
+        file.write(constants_formatted + original_content)
+    title = strip_invalid_filename_characters(textbox_title, max_bytes=30)
+    api = HfApi(token=textbox_token)
+    new_space = api.create_repo(
+        repo_id=f"open-gpt-{title}",
+        repo_type="space",
+        exist_ok=True,
+        private=False,
+        space_sdk="gradio",
+        token=textbox_token,
+    )
+    api.upload_file(
+        repo_id=new_space.repo_id,
+        path_or_fileobj='app.py',
+        path_in_repo='app.py',
+        token=textbox_token,
+        repo_type="space",
+    )
+    api.upload_file(
+        repo_id=new_space.repo_id,
+        path_or_fileobj='README_template.md',
+        path_in_repo='README.md',
+        token=textbox_token,
+        repo_type="space",
+    )
+    huggingface_hub.add_space_secret(
+        new_space.repo_id, "HF_TOKEN", textbox_token, token=textbox_token
+    )
+
+    return gr.Markdown(f"Published to https://huggingface.co/spaces/{new_space.repo_id} ✅", visible=True), gr.Button("Publish", interactive=True)
+
+# Gradio UI setup
+with gr.Blocks(css=css) as demo:
+    # ... (The rest of your Gradio UI setup)
+
+demo.launch(share=True)