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Update app.py
Browse files
app.py
CHANGED
@@ -5,7 +5,7 @@ import time # For potential brief pauses if needed
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# --- Hugging Face Token (Optional but Recommended) ---
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# from huggingface_hub import login
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# login("YOUR_HUGGINGFACE_TOKEN")
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# --- Inference Client ---
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try:
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@@ -15,9 +15,9 @@ try:
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client.timeout = 120 # Increase timeout for potentially long generations
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except Exception as e:
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print(f"Error initializing InferenceClient: {e}")
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client = None
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# --- Parsing Function
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def parse_files(raw_response):
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"""
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Parses filenames and code blocks from the raw AI output.
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@@ -60,16 +60,20 @@ def stream_and_parse_code(prompt, backend, system_message, max_tokens, temperatu
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"""
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Streams raw output to one component and generates final tabs for another.
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This function acts as the main callback for the button click.
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"""
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if not client:
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error_msg = "Error: Inference Client not available."
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yield {
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live_output: error_msg,
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final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Error", children=[gr.Textbox(value=error_msg)])])
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}
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return # Stop execution
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# --- Prepare for Streaming ---
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full_sys_msg = f"""
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You are a code generation AI. Given a prompt, generate the necessary files for a website using the {backend} backend.
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Always include an index.html file.
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@@ -83,9 +87,14 @@ index.html
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style.css
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body {{}}
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Ensure the code is complete. NO commentary, NO explanations, NO markdown formatting like backticks (```).
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Start generating the files now.
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""".strip()
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messages = [
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{"role": "system", "content": full_sys_msg},
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@@ -96,53 +105,55 @@ Start generating the files now.
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error_occurred = False
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error_message = ""
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# Initial state update
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yield {
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live_output: "Generating stream...",
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final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Generating...")])
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}
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# --- Streaming Loop ---
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try:
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stream = client.chat_completion(
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messages,
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max_tokens=int(max_tokens),
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stream=True,
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temperature=temperature,
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top_p=top_p
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)
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for chunk in stream:
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token = chunk.choices[0].delta.content
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if token:
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full_raw_response += token
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# Yield updates for the live raw output component
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# Keep tabs in a '
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yield {
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live_output: full_raw_response,
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final_tabs
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}
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# time.sleep(0.01) # Optional small delay if updates are too fast
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except Exception as e:
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print(f"Error during AI streaming: {e}")
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error_message = f"Error during AI generation: {e}\n\nPartial Response:\n{full_raw_response}"
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error_occurred = True
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# Update live output with error,
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yield {
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live_output: error_message,
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final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Error")])
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}
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-
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# --- Post-Streaming: Parsing and Final Tab Generation ---
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if error_occurred:
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# If an error happened during stream, create
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final_tabs_update = gr.Tabs(tabs=[
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gr.TabItem(label="Error", children=[gr.Textbox(value=error_message, label="Generation Error")])
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])
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else:
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#
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print("\n--- Final Raw AI Response ---")
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print(full_raw_response)
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print("--------------------------\n")
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@@ -150,12 +161,12 @@ Start generating the files now.
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if not files:
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# Handle case where parsing failed or AI gave empty/invalid response
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no_files_msg = "AI finished, but did not return recognizable file content. See raw output above."
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final_tabs_update = gr.Tabs(tabs=[
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gr.TabItem(label="Output", children=[gr.Textbox(value=no_files_msg, label="Result")])
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])
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# Update live output as well
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yield { live_output: full_raw_response + "\n\n" + no_files_msg, final_tabs: final_tabs_update }
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return # Exit if no files
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# --- Create Tabs (if files were parsed successfully) ---
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@@ -163,11 +174,13 @@ Start generating the files now.
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for name, content in files:
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name = name.strip()
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content = content.strip()
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if not name or not content:
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print(f"Skipping file with empty name or content: Name='{name}'")
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continue
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-
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if name.endswith((".html", ".htm")): lang = "html"
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elif name.endswith(".css"): lang = "css"
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elif name.endswith(".js"): lang = "javascript"
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@@ -175,50 +188,95 @@ Start generating the files now.
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elif name.endswith(".json"): lang = "json"
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elif name.endswith(".md"): lang = "markdown"
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elif name.endswith((".sh", ".bash")): lang = "bash"
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tab_item = gr.TabItem(label=name, elem_id=
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gr.Code(value=content, language=lang, label=name)
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])
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tabs_content.append(tab_item)
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-
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else:
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final_tabs_update = gr.Tabs(tabs=tabs_content) # Create the final Tabs component
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# --- Final Update ---
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# Yield the final state for both components
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yield {
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live_output: full_raw_response if not error_occurred else error_message, # Show final raw response or error
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final_tabs: final_tabs_update #
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}
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# --- Gradio UI Definition ---
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with gr.Blocks(css=".gradio-container { max-width: 95% !important; }") as demo: #
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gr.Markdown("## WebGen AI — One Prompt → Full Website Generator")
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gr.Markdown("Generates website code based on your description. Raw output streams live, final files appear in tabs below.")
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with gr.Row():
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with gr.Column(scale=2):
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prompt = gr.Textbox(
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with gr.Accordion("Advanced Options", open=False):
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system_message = gr.Textbox(
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with gr.Column(scale=3):
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gr.Markdown("#### Live Raw Output Stream")
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# Component to show the live, unparsed stream
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live_output = gr.Code(
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gr.Markdown("#### Final Generated Files (Tabs)")
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# Placeholder for the final structured tabs
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final_tabs = gr.Tabs(elem_id="output_tabs")
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@@ -226,10 +284,11 @@ with gr.Blocks(css=".gradio-container { max-width: 95% !important; }") as demo:
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generate_button.click(
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stream_and_parse_code, # Call the main function that handles streaming and parsing
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inputs=[prompt, backend, system_message, max_tokens, temperature, top_p],
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# Outputs dictionary maps function yields to components
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outputs=[live_output, final_tabs],
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show_progress="hidden" # Hide default progress bar as we show live stream
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)
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if __name__ == "__main__":
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demo.launch(debug=True)
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# --- Hugging Face Token (Optional but Recommended) ---
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# from huggingface_hub import login
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# login("YOUR_HUGGINGFACE_TOKEN") # Replace with your token if needed
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# --- Inference Client ---
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try:
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client.timeout = 120 # Increase timeout for potentially long generations
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except Exception as e:
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print(f"Error initializing InferenceClient: {e}")
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client = None # Set client to None if initialization fails
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# --- Parsing Function ---
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def parse_files(raw_response):
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"""
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Parses filenames and code blocks from the raw AI output.
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"""
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Streams raw output to one component and generates final tabs for another.
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This function acts as the main callback for the button click.
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Yields dictionary updates for Gradio components.
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"""
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# Check if client initialized correctly
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if not client:
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error_msg = "Error: Inference Client not available. Check API token or model name."
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# Yield updates to both components indicating the error
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yield {
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live_output: gr.update(value=error_msg),
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final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Error", children=[gr.Textbox(value=error_msg)])])
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}
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return # Stop execution
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# --- Prepare for Streaming ---
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# Construct the system prompt dynamically
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full_sys_msg = f"""
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You are a code generation AI. Given a prompt, generate the necessary files for a website using the {backend} backend.
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Always include an index.html file.
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style.css
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body {{}}
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script.js
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console.log("Hello");
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Ensure the code is complete. NO commentary, NO explanations, NO markdown formatting like backticks (```).
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Start generating the files now.
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""".strip()
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if system_message: # Append user's system message if provided
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full_sys_msg += "\n\n" + system_message
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messages = [
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{"role": "system", "content": full_sys_msg},
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error_occurred = False
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error_message = ""
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# Initial state update: Clear previous output and show generating status
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yield {
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live_output: gr.update(value="Generating stream..."),
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final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Generating...")]) # Indicate loading in tabs
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}
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# --- Streaming Loop ---
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try:
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# Start the streaming call
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stream = client.chat_completion(
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messages,
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max_tokens=int(max_tokens), # Ensure max_tokens is an integer
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stream=True,
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temperature=temperature,
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top_p=top_p
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)
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# Process each chunk received from the stream
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for chunk in stream:
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token = chunk.choices[0].delta.content
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if token:
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full_raw_response += token
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# Yield updates for the live raw output component only
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# Keep tabs in a 'streaming' state during the stream
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yield {
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live_output: gr.update(value=full_raw_response),
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# No update needed for final_tabs here, or keep showing streaming state
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# final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Streaming...")]) # Optional: update tabs state
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}
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# time.sleep(0.01) # Optional: small delay if updates are too fast and causing UI lag
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except Exception as e:
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# Handle errors during the API call or streaming process
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print(f"Error during AI streaming: {e}")
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error_message = f"Error during AI generation: {e}\n\nPartial Response (if any):\n{full_raw_response}"
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error_occurred = True
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# Update live output with error, prepare error tab
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yield {
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live_output: gr.update(value=error_message),
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final_tabs: gr.Tabs(tabs=[gr.TabItem(label="Error")]) # Indicate error state in tabs
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}
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# --- Post-Streaming: Parsing and Final Tab Generation ---
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if error_occurred:
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# If an error happened during stream, create a final error tab
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final_tabs_update = gr.Tabs(tabs=[
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gr.TabItem(label="Error", children=[gr.Textbox(value=error_message, label="Generation Error", lines=10)])
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])
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else:
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# If streaming succeeded, parse the complete raw response
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print("\n--- Final Raw AI Response ---")
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print(full_raw_response)
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print("--------------------------\n")
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if not files:
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# Handle case where parsing failed or AI gave empty/invalid response
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no_files_msg = "AI finished, but did not return recognizable file content or the response was empty. See raw output above."
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final_tabs_update = gr.Tabs(tabs=[
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gr.TabItem(label="Output", children=[gr.Textbox(value=no_files_msg, label="Result")])
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])
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# Update live output as well to make the message clear
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yield { live_output: gr.update(value=full_raw_response + "\n\n" + no_files_msg), final_tabs: final_tabs_update }
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return # Exit if no files
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# --- Create Tabs (if files were parsed successfully) ---
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for name, content in files:
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name = name.strip()
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content = content.strip()
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# Skip if filename or content is empty after stripping
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if not name or not content:
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print(f"Skipping file with empty name or content: Name='{name}'")
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continue
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# Determine language for syntax highlighting
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lang = "plaintext" # Default
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if name.endswith((".html", ".htm")): lang = "html"
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elif name.endswith(".css"): lang = "css"
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elif name.endswith(".js"): lang = "javascript"
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elif name.endswith(".json"): lang = "json"
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elif name.endswith(".md"): lang = "markdown"
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elif name.endswith((".sh", ".bash")): lang = "bash"
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elif name.endswith((".xml", ".xaml", ".svg")): lang = "xml"
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elif name.endswith(".yaml") or name.endswith(".yml"): lang = "yaml"
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# Ensure elem_id is unique and valid (replace problematic characters)
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elem_id = f"tab_{re.sub(r'[^a-zA-Z0-9_-]', '_', name)}"
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tab_item = gr.TabItem(label=name, elem_id=elem_id, children=[
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gr.Code(value=content, language=lang, label=name, interactive=False) # Show code in Code block
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])
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tabs_content.append(tab_item)
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# Handle case where parsing found files, but they were all filtered out (empty name/content)
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if not tabs_content:
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final_tabs_update = gr.Tabs(tabs=[gr.TabItem(label="Output", children=[gr.Textbox(value="No valid files generated after filtering.", label="Result")])])
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else:
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final_tabs_update = gr.Tabs(tabs=tabs_content) # Create the final Tabs component with content
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# --- Final Update ---
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# Yield the final state for both components
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# Use gr.update for live_output if you only want to set its value without recreating it
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# Directly return the new final_tabs component
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yield {
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live_output: gr.update(value=full_raw_response if not error_occurred else error_message), # Show final raw response or error
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final_tabs: final_tabs_update # Update the final_tabs component completely
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}
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# --- Gradio UI Definition ---
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with gr.Blocks(css=".gradio-container { max-width: 95% !important; }") as demo: # Use more screen width
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gr.Markdown("## WebGen AI — One Prompt → Full Website Generator")
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gr.Markdown("Generates website code based on your description. Raw output streams live, final files appear in tabs below.")
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with gr.Row():
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# Column for inputs and controls
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with gr.Column(scale=2):
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prompt = gr.Textbox(
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label="Describe your website",
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placeholder="E.g., a simple landing page for a coffee shop with sections for menu, about, and contact.",
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lines=3 # Allow more lines for the prompt
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)
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backend = gr.Dropdown(
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["Static", "Flask", "Node.js"],
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value="Static",
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label="Backend Technology"
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)
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with gr.Accordion("Advanced Options", open=False):
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system_message = gr.Textbox(
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label="Extra instructions for the AI (System Message)",
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placeholder="Optional: e.g., 'Use Tailwind CSS for styling', 'Make it responsive'",
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value="",
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lines=2
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)
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max_tokens = gr.Slider(
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minimum=256,
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maximum=4096, # Increased max tokens for complex sites
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value=2048, # Increased default
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step=64,
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label="Max Tokens (Output Length)"
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)
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temperature = gr.Slider(
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minimum=0.1,
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maximum=1.5, # Allow slightly higher temperature
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value=0.7,
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step=0.1,
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label="Temperature (Creativity)"
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)
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top_p = gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (Sampling Focus)"
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)
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generate_button = gr.Button("✨ Generate Code ✨", variant="primary") # Make button primary
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# Column for live output
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with gr.Column(scale=3):
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gr.Markdown("#### Live Raw Output Stream")
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# Component to show the live, unparsed stream - CORRECTED LANGUAGE
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live_output = gr.Code(
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label="Raw AI Stream",
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language="plaintext", # Use "plaintext" for generic text
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lines=20, # Increased lines for visibility
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interactive=False # Output only
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)
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gr.Markdown("---") # Separator
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gr.Markdown("#### Final Generated Files (Tabs)")
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# Placeholder for the final structured tabs - will be replaced by the output yield
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final_tabs = gr.Tabs(elem_id="output_tabs")
|
281 |
|
282 |
|
|
|
284 |
generate_button.click(
|
285 |
stream_and_parse_code, # Call the main function that handles streaming and parsing
|
286 |
inputs=[prompt, backend, system_message, max_tokens, temperature, top_p],
|
287 |
+
# Outputs dictionary maps function yields to components by variable name
|
288 |
outputs=[live_output, final_tabs],
|
289 |
+
show_progress="hidden" # Hide default Gradio progress bar as we show live stream
|
290 |
)
|
291 |
|
292 |
if __name__ == "__main__":
|
293 |
+
# Launch the Gradio app with debug=True for development
|
294 |
demo.launch(debug=True)
|