Rename app.js to app.py

app.js

@@ -1,53 +0,0 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-# Choose a lightweight, open model
-model_name = "mistralai/Mistral-7B-Instruct-v0.2"
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-
-pipe = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_new_tokens=256,
-    do_sample=True,
-    temperature=0.7,
-    top_p=0.9
-)
-
-def chat(history, message):
-    # Build conversation text
-    prompt = ""
-    for user, bot in history:
-        prompt += f"User: {user}\nAssistant: {bot}\n"
-    prompt += f"User: {message}\nAssistant:"
-
-    output = pipe(prompt)[0]["generated_text"]
-    reply = output.split("Assistant:")[-1].strip()
-
-    history.append((message, reply))
-    return history, ""
-
-with gr.Blocks() as demo:
-    gr.Markdown("# 🔥 My Chatbot")
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(label="Say something")
-    clear = gr.Button("Clear chat")
-
-    state = gr.State([])
-
-    def respond(message, history):
-        if history is None:
-            history = []
-        return chat(history, message)
-
-    msg.submit(respond, [msg, chatbot], [chatbot, msg])
-    clear.click(lambda: ([], ""), None, [chatbot, msg])
-
-demo.launch()

app.py

@@ -0,0 +1,192 @@
+import gradio as gr
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# --- LANGFUSE SETUP ---
+# We use the drop-in OpenAI client. This automatically traces all model calls.
+# We removed the @observe decorator from the function to prevent Gradio conflicts.
+try:
+    from langfuse.openai import OpenAI
+    print("✅ SUCCESS: Langfuse OpenAI client loaded.")
+    LANGFUSE_ACTIVE = True
+except ImportError as e:
+    print(f"⚠️ WARNING: Langfuse not found ({e}).")
+    print("ℹ️ FALLBACK: Switching to standard OpenAI.")
+    from openai import OpenAI
+    LANGFUSE_ACTIVE = False
+# ----------------------
+
+SYSTEM_PROMPT = os.getenv("XTRNPMT")
+
+API_BASE_URL = "https://api.featherless.ai/v1"
+
+FEATHERLESS_API_KEY = os.getenv("FEATHERLESS_API_KEY")
+
+FEATHERLESS_MODEL = "darkc0de/XortronCriminalComputingConfig"
+
+if not FEATHERLESS_API_KEY:
+    print("WARNING: FEATHERLESS_API_KEY environment variable is not set.")
+
+try:
+    if not FEATHERLESS_API_KEY:
+        raise ValueError("FEATHERLESS_API_KEY is not set. Please set it as an environment variable or a secret in your deployment environment.")
+
+    # Client initialization
+    # If Langfuse is active, this client automatically logs to Langfuse.
+    client = OpenAI(
+        base_url=API_BASE_URL,
+        api_key=FEATHERLESS_API_KEY
+    )
+    print(f"OpenAI client initialized with base_url: {API_BASE_URL} for Featherless AI, model: {FEATHERLESS_MODEL}")
+
+except Exception as e:
+    print(f"Error initializing OpenAI client with base_url '{API_BASE_URL}': {e}")
+    raise RuntimeError(
+        "Could not initialize OpenAI client. "
+        f"Please check the API base URL ('{API_BASE_URL}'), your Featherless AI API key, model ID, "
+        f"and ensure the server is accessible. Original error: {e}"
+    )
+
+
+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 Featherless AI API.
+    """
+    # 32k tokens is roughly 128,000 characters. 
+    # We cap the context at 100,000 characters (~25k tokens) to leave 7k tokens safely for the AI's response generation.
+    MAX_CONTEXT_CHARS = 100000 
+    
+    messages = [{"role": "system", "content": SYSTEM_PROMPT or ""}]
+
+    # 1. Calculate how many characters we have available for the chat history
+    system_chars = len(SYSTEM_PROMPT or "")
+    message_chars = len(message or "")
+    allowed_history_chars = MAX_CONTEXT_CHARS - system_chars - message_chars
+
+    # 2. Iterate backwards through history to only keep the most recent messages that fit
+    recent_history = []
+    current_hist_chars = 0
+    
+    # In Gradio 6.0, history is a list of dicts: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
+    for msg in reversed(history):
+        content = msg.get("content", "") or ""
+        role = msg.get("role", "user")
+        
+        turn_chars = len(content)
+        
+        # Truncate older messages if appending them exceeds our safe limit
+        if current_hist_chars + turn_chars > allowed_history_chars:
+            break
+            
+        recent_history.insert(0, {"role": role, "content": content})
+        current_hist_chars += turn_chars
+
+    # 3. Append the filtered history and the newest user message
+    messages.extend(recent_history)
+    messages.append({"role": "user", "content": message})
+
+    response_text = ""
+
+    try:
+        # Optional: Add a name to the trace if Langfuse is active
+        kwargs = {}
+        if LANGFUSE_ACTIVE:
+            kwargs["name"] = "featherless-generation"
+
+        stream = client.chat.completions.create(
+            messages=messages,
+            model=FEATHERLESS_MODEL,
+            temperature=0.7,  # Changed to 0.85
+            top_p=0.95,        # Set top_p to 0.95
+            frequency_penalty=0.1,
+            presence_penalty=0,
+            stream=True,
+            **kwargs
+        )
+
+        for chunk in stream:
+            # Check if there are choices and if the delta has content
+            if chunk.choices and len(chunk.choices) > 0:
+                delta = chunk.choices[0].delta
+                if hasattr(delta, "content") and delta.content is not None:
+                    response_text += delta.content
+                    yield response_text
+
+    except Exception as e:
+        error_message = f"An error occurred during model inference with Featherless AI: {e}"
+        print(error_message)
+        yield error_message
+
+
+kofi_script = """
+<script src='https://storage.ko-fi.com/cdn/scripts/overlay-widget.js'></script>
+<script>
+  kofiWidgetOverlay.draw('xortron', {
+    'type': 'floating-chat',
+    'floating-chat.donateButton.text': 'Support me',
+    'floating-chat.donateButton.background-color': '#794bc4',
+    'floating-chat.donateButton.text-color': '#fff'
+  });
+</script>
+"""
+
+# Changed width of the image to 50%
+footer_image_html = """
+<div style="width: 100%; text-align: center; margin-top: 10px;">
+    <a href="https://ko-fi.com/Z8Z51E5TIG" target="_blank" rel="noopener noreferrer">
+        <img src="https://huggingface.co/spaces/xortron/chat/resolve/main/HiQrS.gif" alt="Support Xortron on Ko-fi" style="width: 70%; height: auto; display: block; border: none; margin: 0 auto;">
+    </a>
+</div>
+"""
+
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700&display=swap');
+body, .gradio-container {
+    font-family: 'Orbitron', sans-serif !important;
+}
+.gr-button { font-family: 'Orbitron', sans-serif !important; }
+.gr-input { font-family: 'Orbitron', sans-serif !important; }
+.gr-label { font-family: 'Orbitron', sans-serif !important; }
+.gr-chatbot .message { font-family: 'Orbitron', sans-serif !important; }
+
+/* --- HIDE THE HUGGING FACE SPACES HEADER --- */
+#huggingface-spaces-header, 
+#spaces-header, 
+spaces-header, 
+.spaces-header {
+    display: none !important;
+}
+"""
+
+with gr.Blocks(title="XORTRON") as demo:
+    
+    gr.ChatInterface(
+        fn=respond,               # The function to call when a message is sent
+        chatbot=gr.Chatbot(       # Configure the chatbot display area
+            height=800,           # Set the height of the chat history display to 800px
+            label="XORTRON - Criminal Computing"  # Set the label
+        )
+    )
+
+    # Added the clickable header image below the chat window
+    gr.HTML(footer_image_html)
+
+
+if __name__ == "__main__":
+    if not FEATHERLESS_API_KEY:
+        print("\nCRITICAL ERROR: FEATHERLESS_API_KEY is not set.")
+        print("Please ensure it's set as a secret in your Hugging Face Space settings or as an environment variable.\n")
+
+    try:
+        demo.queue(default_concurrency_limit=2)
+
+        demo.launch(share=False, theme="Nymbo/Nymbo_Theme", head=kofi_script, css=custom_css)
+    except NameError as ne:
+        print(f"Gradio demo could not be launched. 'client' might not have been initialized: {ne}")
+    except RuntimeError as re:
+        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}")