Update app.py

app.py

@@ -1,29 +1,18 @@
 import openai, gradio as gr, json, plotly.graph_objects as go
+from pathlib import Path
 
-SYSTEM_PROMPT = """
-You are ZEN Multimodal Assistant created by ZEN AI Co.
-Choose the single best modality for each reply:
-
-1. Image – when an illustration, diagram, or photo-realistic scene answers best.  
-   Respond **only** with JSON:
-   {"type":"image","prompt":"<detailed prompt for DALL-E-3>"}
-
-2. Chart – when the user requests or clearly needs data-visualisation.  
-   Respond **only** with JSON:
-   {"type":"chart",
-    "title":"<chart title>",
-    "data":[
-        {"x":[...], "y":[...], "label":"<series name>"},
-        ...
-    ]}
-
-3. Text – for every other case.  
-   Respond with plain language (no JSON).
+# --- Style: load from local CSS file ---
+CUSTOM_CSS = Path("style.css").read_text()
 
-Never wrap JSON in markdown fences and never add comments.
+SYSTEM_PROMPT = """
+You are ZEN Multimodal Assistant by ZEN AI Co.
+Choose only ONE of these output modes per reply:
+- Image: when a visual or illustration is most useful. Respond only with JSON: {"type":"image","prompt":"<prompt for DALL-E-3>"}
+- Chart: when a user requests or needs a data visualization. Respond only with JSON: {"type":"chart","title":"<chart title>","data":[{"x":[...], "y":[...], "label":"<series name>"}]}
+- Text: for all other situations, reply with a helpful, complete, conversational answer. Never reply with the word "text" or any label, just the response itself. Never reply in JSON unless for image or chart.
+Never use markdown code fences, never add comments.
 """
 
-# ---------- Helper functions ---------- #
 def build_messages(history, user_msg):
     messages = [{"role": "system", "content": SYSTEM_PROMPT}]
     for u, a in history:
@@ -32,7 +21,6 @@ def build_messages(history, user_msg):
     messages.append({"role": "user", "content": user_msg})
     return messages
 
-
 def multimodal_chat(api_key, user_msg, history):
     if not api_key:
         raise gr.Error("🔑  Please paste your OpenAI API key first.")
@@ -40,27 +28,29 @@ def multimodal_chat(api_key, user_msg, history):
 
     history = history or []
     messages = build_messages(history, user_msg)
-
     response = openai.chat.completions.create(
-        model="gpt-4o-mini",
+        model="gpt-4o",
         messages=messages,
-        temperature=0.7,
+        temperature=0.6,
     )
     assistant_content = response.choices[0].message.content.strip()
 
+    # Defensive: Never show the word "text" alone
+    if assistant_content.lower() == "text":
+        assistant_content = "(I'm sorry, I didn't understand. Could you rephrase?)"
+
     img_url, fig = None, None
-    try:                                                # JSON branch (image or chart)
+    try:  # Try to parse as JSON for image or chart
         parsed = json.loads(assistant_content)
         if parsed.get("type") == "image":
             dalle = openai.images.generate(
                 model="dall-e-3",
-                prompt=parsed.get("prompt", "high-quality illustration"),
+                prompt=parsed.get("prompt", "high quality illustration, cinematic, best quality"),
                 n=1,
                 size="1024x1024",
             )
             img_url = dalle.data[0].url
             history.append([user_msg, f"![generated image]({img_url})"])
-
         elif parsed.get("type") == "chart":
             fig = go.Figure()
             for s in parsed["data"]:
@@ -74,41 +64,35 @@ def multimodal_chat(api_key, user_msg, history):
                 )
             fig.update_layout(title=parsed.get("title", "Chart"))
             history.append([user_msg, parsed.get("title", "Chart below")])
-
-        else:                                           # fallback to text
-            history.append([user_msg, assistant_content])
-
-    except (json.JSONDecodeError, KeyError, TypeError): # plain-text branch
+        else:
+            # If unexpected JSON, fallback to text
+            history.append([user_msg, str(assistant_content)])
+    except (json.JSONDecodeError, KeyError, TypeError):
+        # If not JSON, treat as text
         history.append([user_msg, assistant_content])
 
     return history, img_url, fig
 
-
-# ---------- Gradio UI ---------- #
-with gr.Blocks(title="ZEN Multimodal Assistant") as demo:
+with gr.Blocks(title="ZEN Multimodal Assistant", css=CUSTOM_CSS) as demo:
     gr.Markdown(
         """
         # 🧠 ZEN Multimodal Assistant  
-        Paste your OpenAI API key once per session and chat freely –  
-        the assistant decides whether to answer with **text, an image, or an interactive chart**.
+        Paste your OpenAI API key (never saved).  
+        This assistant intelligently responds with **text, an image, or an interactive chart**.
         """
     )
-
     api_key   = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...")
     chatbot   = gr.Chatbot(label="Conversation")
     with gr.Row():
         user_msg = gr.Textbox(placeholder="Ask me anything…", label="Your message", scale=4)
         send_btn = gr.Button("Send", variant="primary")
-
     img_out   = gr.Image(label="Generated image")
     chart_out = gr.Plot(label="Interactive chart")
 
     def respond(api_key, user_msg, chat_history):
         chat_history, img_url, fig = multimodal_chat(api_key, user_msg, chat_history)
-
         img_update  = gr.update(value=img_url) if img_url else gr.update(value=None)
         fig_update  = gr.update(value=fig)     if fig     else gr.update(value=None)
-
         return chat_history, img_update, fig_update
 
     send_btn.click(