Update app.py

app.py

@@ -2,31 +2,30 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import time
 
-# Clientes
-chat_client = InferenceClient("lambdaindie/lambdai")
-image_client = InferenceClient("stabilityai/stable-diffusion-2")
+client = InferenceClient("lambdaindie/lambdai")
 
-# CSS com JetBrains Mono forçado
 css = """
+@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono&display=swap');
+
+* {
+    font-family: 'JetBrains Mono', monospace !important;
+}
+
 body {
-    font-family: 'JetBrains Mono', monospace;
     background-color: #111;
     color: #e0e0e0;
 }
-.gr-textbox textarea {
-    background-color: #181818 !important;
-    color: #fff !important;
-    font-family: 'JetBrains Mono', monospace;
-    border-radius: 8px;
-}
+
 .markdown-think {
     background-color: #1e1e1e;
     border-left: 4px solid #555;
     padding: 10px;
     margin-bottom: 8px;
     font-style: italic;
+    white-space: pre-wrap;
     animation: pulse 1.5s infinite ease-in-out;
 }
+
 @keyframes pulse {
     0% { opacity: 0.6; }
     50% { opacity: 1.0; }
@@ -43,14 +42,15 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         if assistant:
             messages.append({"role": "assistant", "content": assistant})
 
-    thinking_prompt = messages + [
-        {"role": "user", "content": f"{message}\n\nThink step-by-step before answering."}
-    ]
+    thinking_prompt = messages + [{
+        "role": "user",
+        "content": f"{message}\n\nThinking step-by-step before answering."
+    }]
 
     reasoning = ""
     yield '<div class="markdown-think">Thinking...</div>'
 
-    for chunk in chat_client.chat_completion(
+    for chunk in client.chat_completion(
         thinking_prompt,
         max_tokens=max_tokens,
         stream=True,
@@ -59,7 +59,8 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
     ):
         token = chunk.choices[0].delta.content or ""
         reasoning += token
-        yield f'<div class="markdown-think">{reasoning.strip()}</div>'
+        styled_thought = f'<div class="markdown-think">{reasoning.strip()}</div>'
+        yield styled_thought
 
     time.sleep(0.5)
 
@@ -70,7 +71,7 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
     ]
 
     final_answer = ""
-    for chunk in chat_client.chat_completion(
+    for chunk in client.chat_completion(
         final_prompt,
         max_tokens=max_tokens,
         stream=True,
@@ -81,34 +82,19 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         final_answer += token
         yield final_answer.strip()
 
-def generate_image(prompt):
-    return image_client.text_to_image(prompt, guidance_scale=7.5)
-
-# Interface
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("# λmabdAI")
-
-    with gr.Tabs():
-        with gr.Tab("Chat"):
-            gr.ChatInterface(
-                fn=respond,
-                additional_inputs=[
-                    gr.Textbox(
-                        value="You are a concise, logical AI that explains its reasoning clearly before answering.",
-                        label="System Message"
-                    ),
-                    gr.Slider(64, 2048, value=512, step=1, label="Max Tokens"),
-                    gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
-                    gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
-                ]
-            )
-
-        with gr.Tab("Image Generator"):
-            gr.Markdown("### Generate an image from a prompt")
-            prompt = gr.Textbox(label="Prompt")
-            output = gr.Image(type="pil")
-            btn = gr.Button("Generate")
-            btn.click(fn=generate_image, inputs=prompt, outputs=output)
+demo = gr.ChatInterface(
+    fn=respond,
+    title="λambdAI",
+    theme=gr.themes.Base(),
+    css=css,
+    additional_inputs=[
+        gr.Textbox(value="You are a concise, logical AI that explains its reasoning clearly before answering.",
+                   label="System Message"),
+        gr.Slider(64, 2048, value=512, step=1, label="Max Tokens"),
+        gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
+    ]
+)
 
 if __name__ == "__main__":
     demo.launch()