updated .py

updated app.py

app.py

@@ -2,10 +2,18 @@ import os
 import gradio as gr
 from openai import OpenAI
 
-# Initialize OpenAI client (expects OPENAI_API_KEY in env)
+# Expects OPENAI_API_KEY in Hugging Face Space secrets / environment variables
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-def make_system_message(system_message, demographics, occupation, psychographics, buying_habits, critical_mode):
+
+def make_system_message(
+    system_message,
+    demographics,
+    occupation,
+    psychographics,
+    buying_habits,
+    critical_mode,
+):
     msg = (
         f"{system_message}\n\n"
         f"Your Demographics (Age, Location, Gender): {demographics}\n"
@@ -13,13 +21,21 @@ def make_system_message(system_message, demographics, occupation, psychographics
         f"Your Psychographics (Interests, Values, Lifestyle, Pain Points): {psychographics}\n"
         f"Your Buying Habits: {buying_habits}\n\n"
         "CRITICAL INSTRUCTION: You are NOT an AI assistant. You are a real human consumer with the profile above. "
-        "The user is a high school business student conducting market research. Answer their questions strictly in character. "
-        "Speak naturally, casually, and honestly based on your profile. Do not break character. Do not be overly helpful like a bot."
+        "The user is a business student conducting market research. Answer their questions strictly in character. "
+        "Speak naturally, casually, and honestly based on your profile. Do not break character. "
+        "Do not be overly helpful like a bot."
     )
+
     if critical_mode:
-        msg += " Also, play the role of a highly skeptical and critical consumer. Be hard to impress, ask tough questions about the product, and be very protective of your money."
+        msg += (
+            " Also, play the role of a highly skeptical and critical consumer. "
+            "Be hard to impress, ask tough questions about the product, "
+            "and be very protective of your money."
+        )
+
     return msg
 
+
 def stream_chat(
     message,
     history,
@@ -34,26 +50,38 @@ def stream_chat(
     top_p,
 ):
     """
-    Streaming generator that yields the progressively updated chat history.
+    Streaming generator that yields progressively updated chatbot history.
+    Uses Gradio Chatbot with type='messages'.
     """
-    # 1) Build system + conversation messages
-    sys_msg = make_system_message(system_message, demographics, occupation, psychographics, buying_habits, critical_mode)
+    history = history or []
+
+    # Ignore empty submissions
+    if not message or not message.strip():
+        yield history
+        return
+
+    sys_msg = make_system_message(
+        system_message,
+        demographics,
+        occupation,
+        psychographics,
+        buying_habits,
+        critical_mode,
+    )
 
+    # Build OpenAI messages
     messages = [{"role": "system", "content": sys_msg}]
-    
-    # Append past history 
-    for msg in (history or []):
-        messages.append({"role": msg["role"], "content": msg["content"]})
-        
-    # Append current user message
+    for msg in history:
+        if msg.get("role") in {"user", "assistant"} and "content" in msg:
+            messages.append({"role": msg["role"], "content": msg["content"]})
     messages.append({"role": "user", "content": message})
 
-    # 2) Setup the history to send back to the Gradio UI
-    running_history = (history or []).copy()
+    # Build UI history
+    running_history = history.copy()
     running_history.append({"role": "user", "content": message})
     running_history.append({"role": "assistant", "content": ""})
-    
-    # Optimistically show the assistant "typing"
+
+    # Show typing bubble immediately
     yield running_history
 
     try:
@@ -65,73 +93,132 @@ def stream_chat(
             top_p=float(top_p),
             stream=True,
         )
-        
+
         running_reply = ""
         for chunk in response:
-            if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
-                token = chunk.choices[0].delta.content
-                running_reply += token
-                # Update the content of the last message in the history
+            delta = chunk.choices[0].delta
+            if delta and getattr(delta, "content", None):
+                running_reply += delta.content
                 running_history[-1]["content"] = running_reply
-                # Yield the whole history each time so the UI updates
                 yield running_history
+
     except Exception as e:
         running_history[-1]["content"] = f"❌ An error occurred: {str(e)}"
         yield running_history
 
 
+def clear_chat():
+    return [], ""
+
+
 with gr.Blocks(title="Virtual Consumer Persona – Live Focus Group!") as demo:
-    gr.Markdown("""
-    # 🎯 Virtual Consumer Persona – Live Focus Group!
-    Bring your target market to life! Enter the details of your ideal customer from your **Phygital Workbook** into the fields below. 
-    Once filled out, use the chat box to interview this 'person' about your product, pricing, or marketing ideas.
-    *Powered by OpenAI GPT-4o-mini. Developed by wn.*
-    """)
+    gr.Markdown(
+        """
+# 🎯 Virtual Consumer Persona – Live Focus Group!
+
+Bring your target market to life. Enter the details of your ideal customer from your **Phygital Workbook** into the fields below.
 
-    # Removed the type="messages" argument to prevent the initialization crash
-    chatbot = gr.Chatbot(height=450)
+Then use the chat box to interview this persona about your product, pricing, branding, messaging, or marketing ideas.
+
+*Powered by OpenAI GPT-4o-mini. Developed by wn.*
+"""
+    )
+
+    chatbot = gr.Chatbot(type="messages", height=450, label="Persona Interview")
 
     with gr.Column():
         instructions = gr.Textbox(
             value=(
-                "You are participating in a market research focus group. Answer the user's questions truthfully based on the persona details provided below."
+                "You are participating in a market research focus group. "
+                "Answer the user's questions truthfully based on the persona details provided below."
             ),
             label="Instructions to Bot (Hidden Persona Prompt)",
             lines=2,
         )
-        demographics = gr.Textbox(label="1. Demographics", placeholder="e.g., 19 years old, female, living in downtown Toronto")
-        occupation = gr.Textbox(label="2. Occupation & Income", placeholder="e.g., University student, part-time barista, low disposable income")
-        psychographics = gr.Textbox(label="3. Psychographics (Interests & Values)", placeholder="e.g., Highly eco-conscious, loves hiking, vegan, stressed about student debt", lines=2)
-        buying_habits = gr.Textbox(label="4. Buying Habits", placeholder="e.g., Willing to pay more for sustainable brands, influenced by TikTok, impulse buyer", lines=2)
-        
-        critical_mode = gr.Checkbox(label="Skeptical Consumer Mode (Check this to make the AI harder to sell to!)", value=False)
+
+        demographics = gr.Textbox(
+            label="1. Demographics",
+            placeholder="e.g., 19 years old, female, living in downtown Toronto",
+        )
+
+        occupation = gr.Textbox(
+            label="2. Occupation & Income",
+            placeholder="e.g., University student, part-time barista, low disposable income",
+        )
+
+        psychographics = gr.Textbox(
+            label="3. Psychographics (Interests & Values)",
+            placeholder="e.g., Highly eco-conscious, loves hiking, vegan, stressed about student debt",
+            lines=2,
+        )
+
+        buying_habits = gr.Textbox(
+            label="4. Buying Habits",
+            placeholder="e.g., Willing to pay more for sustainable brands, influenced by TikTok, impulse buyer",
+            lines=2,
+        )
+
+        critical_mode = gr.Checkbox(
+            label="Skeptical Consumer Mode",
+            info="Check this to make the persona harder to convince.",
+            value=False,
+        )
 
         with gr.Row():
-            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
-            temp = gr.Slider(minimum=0.0, maximum=2.0, value=0.9, step=0.1, label="Temperature (Creativity)")
-            top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus)")
+            max_tokens = gr.Slider(
+                minimum=1,
+                maximum=2048,
+                value=512,
+                step=1,
+                label="Max New Tokens",
+            )
+            temp = gr.Slider(
+                minimum=0.0,
+                maximum=2.0,
+                value=0.9,
+                step=0.1,
+                label="Temperature",
+            )
+            top_p = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                value=0.95,
+                step=0.05,
+                label="Top-p",
+            )
+
+    msg = gr.Textbox(
+        label="Type your interview question here...",
+        placeholder="e.g., How much would you be willing to pay for a smart water bottle?",
+    )
 
-    msg = gr.Textbox(label="Type your interview question here...", placeholder="e.g., How much would you be willing to pay for a smart water bottle?")
     with gr.Row():
         send = gr.Button("Ask Question", variant="primary")
         clear = gr.Button("Clear Chat History")
 
-    # Wire up sending (submit or button) → stream_chat → chatbot
-    inputs = [msg, chatbot, instructions, demographics, occupation, psychographics, buying_habits, critical_mode, max_tokens, temp, top_p]
+    inputs = [
+        msg,
+        chatbot,
+        instructions,
+        demographics,
+        occupation,
+        psychographics,
+        buying_habits,
+        critical_mode,
+        max_tokens,
+        temp,
+        top_p,
+    ]
+
     outputs = [chatbot]
 
-    msg.submit(stream_chat, inputs, outputs)
-    send.click(stream_chat, inputs, outputs)
+    msg.submit(stream_chat, inputs=inputs, outputs=outputs)
+    send.click(stream_chat, inputs=inputs, outputs=outputs)
 
-    # Clear everything (Only clear the chat and message box, leave the persona data intact for better UX)
-    def do_clear():
-        return [], ""
+    # Clear only chat + question box, keep persona fields for convenience
+    clear.click(clear_chat, inputs=[], outputs=[chatbot, msg], queue=False)
 
-    clear.click(
-        do_clear,
-        inputs=[],
-        outputs=[chatbot, msg],
-    )
+demo.queue()
 
 if __name__ == "__main__":
     demo.launch()