Upload app (4).py

app (4).py

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+# Import the Gradio library for creating the web interface
+import gradio as gr
+# Import the InferenceClient from huggingface_hub to interact with the language model
+from huggingface_hub import InferenceClient
+
+# --- Configuration Constants ---
+# Define the maximum number of tokens the model should generate in a single response
+FIXED_MAX_TOKENS = 1000 # Note: This is a very high value, typical values are much lower (e.g., 512, 1024, 2048, 4096 for many models)
+
+
+# --- Initialize the InferenceClient ---
+# For custom OpenAI-compatible APIs, initialize the InferenceClient with the base URL.
+# The specific model will be specified in the API call (e.g., chat_completion).
+API_BASE_URL = "https://vulture-awake-probably.ngrok-free.app/v1/chat/completions" # Base URL for the custom API
+
+try:
+    # Initialize the client with the base URL of your API.
+    # If your API requires an authentication token, you might need to pass it here,
+    # e.g., client = InferenceClient(base_url=API_BASE_URL, token="YOUR_API_TOKEN")
+    # or ensure it's set as an environment variable if the client/API supports that.
+    client = InferenceClient(base_url=API_BASE_URL)
+    print(f"InferenceClient initialized with base_url: {API_BASE_URL}")
+except Exception as e:
+    print(f"Error initializing InferenceClient with base_url '{API_BASE_URL}': {e}")
+    # Handle the error appropriately, e.g., by exiting or using a fallback
+    raise RuntimeError(
+        "Could not initialize InferenceClient. "
+        f"Please check the API base URL ('{API_BASE_URL}') and ensure the server is accessible. "
+        f"Error: {e}"
+    )
+
+
+# --- Core Chatbot Logic ---
+def respond(message, history):
+    """
+    This function processes the user's message and the chat history to generate a response
+    from the language model using the custom API.
+
+    Args:
+        message (str): The latest message from the user.
+        history (list of lists): A list where each inner list contains a pair of
+                                 [user_message, ai_message].
+
+    Yields:
+        str: The generated response token by token (for streaming).
+    """
+    # Initialize the messages list
+    messages = []
+
+    # Append past interactions from the history to the messages list
+    # This provides context to the language model
+    for user_message, ai_message in history:
+        if user_message: # Ensure there's a user message
+            messages.append({"role": "user", "content": user_message})
+        if ai_message: # Ensure there's an AI message
+            messages.append({"role": "assistant", "content": ai_message})
+
+    # Append the current user's message to the messages list
+    messages.append({"role": "user", "content": message})
+
+    # Initialize an empty string to accumulate the response
+    response_text = ""
+
+    try:
+        # Make a streaming call to the language model's chat completions endpoint.
+        # The `model` parameter specifies which model to use at the endpoint.
+        stream = client.chat_completion(
+            messages=messages,              # The conversation history and current message
+            max_tokens=FIXED_MAX_TOKENS,    # Maximum tokens for the response
+            stream=True,                    # Enable streaming for token-by-token output
+        )
+
+        for chunk in stream:
+            # Check if the chunk contains content and the content is not None
+            # The exact structure of the chunk can vary based on the model/endpoint
+            if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content is not None:
+                token = chunk.choices[0].delta.content # Extract the token from the chunk
+                response_text += token                 # Append the token to the response string
+                yield response_text                  # Yield the accumulated response so far (for streaming UI update)
+
+    except Exception as e:
+        # If any error occurs during the API call, yield an error message
+        error_message = f"An error occurred during model inference: {e}"
+        print(error_message) # Also print to console for debugging
+        yield error_message
+
+# --- Gradio Interface Definition ---
+
+# URL for the header image
+header_image_path = "https://cdn-uploads.huggingface.co/production/uploads/6540a02d1389943fef4d2640/j61iZTDaK9g0UW3aWGwWi.gif"
+
+# Ko-fi widget script
+kofi_script = """
+<script src='https://storage.ko-fi.com/cdn/scripts/overlay-widget.js'></script>
+<script>
+  kofiWidgetOverlay.draw('sonnydesorbo', {
+    'type': 'floating-chat',
+    'floating-chat.donateButton.text': 'Support me',
+    'floating-chat.donateButton.background-color': '#00b9fe',
+    'floating-chat.donateButton.text-color': '#fff'
+  });
+</script>
+"""
+
+# Ko-fi button HTML
+kofi_button_html = """
+<div style="text-align: center; padding: 20px;">
+    <a href='https://ko-fi.com/Z8Z51E5TIG' target='_blank'>
+        <img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi5.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' />
+    </a>
+</div>
+"""
+
+# Create a Gradio Blocks layout for more control over the interface
+# theme=gr.themes.Soft() applies a soft visual theme
+# Add the kofi_script to the head of the HTML page
+with gr.Blocks(theme=gr.themes.Soft(), head=kofi_script) as demo:
+    # Display an image at the top of the chatbot interface
+    gr.Image(
+        value=header_image_path, # Source of the image
+        label="Chatbot Header",   # Alt text or label (not shown due to show_label=False)
+        show_label=False,         # Hide the label text
+        interactive=False,        # Make the image non-interactive
+        height=150,               # Set the height of the image
+        elem_id="chatbot-logo"    # Assign an HTML ID for potential CSS styling
+    )
+
+    # Create the chat interface component
+    gr.ChatInterface(
+        fn=respond,               # The function to call when a message is sent
+        chatbot=gr.Chatbot(       # Configure the chatbot display area
+            height=650            # Set the height of the chat history display
+        ),
+        # Additional parameters for ChatInterface can be added here, e.g.:
+        # title="Xortron7 Chat",
+        # description="Chat with Xortron7, your AI assistant.",
+        # examples=[["Hello!", None], ["What is Gradio?", None]],
+        # retry_btn=None, # Removes the retry button
+        # undo_btn="Delete Previous", # Customizes the undo button
+        # clear_btn="Clear Chat", # Customizes the clear button
+    )
+
+    # Add the Ko-fi button at the bottom
+    gr.HTML(kofi_button_html) #
+
+# --- Application Entry Point ---
+if __name__ == "__main__":
+    # Launch the Gradio web server
+    # show_api=False disables the API documentation page
+    # share=False prevents creating a public Gradio link (for local development)
+    try:
+        demo.launch(show_api=False, share=False)
+    except NameError as ne:
+        # This might happen if 'client' was not defined due to an error during initialization
+        print(f"Gradio demo could not be launched. 'client' might not have been initialized: {ne}")
+    except RuntimeError as re:
+        # This catches the RuntimeError raised if client initialization failed explicitly
+        print(f"Gradio demo could not be launched due to an error during client initialization: {re}")
+    except Exception as e:
+        print(f"An unexpected error occurred when trying to launch Gradio demo: {e}")