Update app.py

app.py

@@ -57,6 +57,7 @@ def generate_response(messages, max_length=512, temperature=0.7, top_p=0.9):
 def process_input(
     analysis_mode, # Mode selector
     player_stats,
+    player_behavior_input,
     system_prompt, # Single system prompt from UI
     max_length,
     temperature,
@@ -70,13 +71,17 @@ def process_input(
     if system_prompt and system_prompt.strip():
         messages.append({"role": "system", "content": system_prompt})
     
-    # Add an empty user message or specific content based on analysis mode
+    # Add content based on analysis mode (no empty content for any mode)
     if analysis_mode == "Frequency Only":
         user_content = f"Player Move Frequency Stats (Long-Term):\n{player_stats}"
         messages.append({"role": "user", "content": user_content})
-    else:  # For Markov Prediction or any other mode
-        # Just add an empty user message to trigger the model response
-        messages.append({"role": "user", "content": ""})
+    elif analysis_mode == "Behavior Analysis":
+        user_content = player_behavior_input
+        messages.append({"role": "user", "content": user_content})
+    else:  # For Markov Prediction only mode
+        # Don't add any user message - let system prompt handle everything
+        user_content = ""
+        # Note: We're not appending an empty user message here
 
     # --- Time Measurement Start ---
     start_time = time.time()
@@ -94,10 +99,10 @@ def process_input(
     duration = round(end_time - start_time, 2)
 
     # For display purposes - show what was actually sent to the model
-    if analysis_mode == "Frequency Only":
+    if user_content:
         display_prompt = f"Selected Mode: {analysis_mode}\nSystem Prompt:\n{system_prompt}\n\n------\n\nUser Content:\n{user_content}"
     else:
-        display_prompt = f"Selected Mode: {analysis_mode}\nSystem Prompt:\n{system_prompt}\n\n------\n\nUser Content:\n[Empty]"
+        display_prompt = f"Selected Mode: {analysis_mode}\nSystem Prompt:\n{system_prompt}"
 
     print(f"Processing finished in {duration} seconds.")
     # Return all results including time and tokens
@@ -127,7 +132,7 @@ Step 4: State Final Recommendation.
 Base your analysis strictly on the provided frequencies and the stated RPS rules.
 """
 
-# Updated Markov system prompt with the working version
+# Updated Markov system prompt - no changes to content
 DEFAULT_SYSTEM_PROMPT_MARKOV = """You are analyzing a Rock-Paper-Scissors (RPS) game using a Markov transition matrix.
 
 ### TRANSITION MATRIX:
@@ -169,18 +174,51 @@ Predicted Next Move: [Move with highest probability]
 Optimal Counter: [Move that beats the predicted move]
 """
 
+# New Behavior Analysis prompt
+DEFAULT_SYSTEM_PROMPT_BEHAVIOR = """You are an RPS assistant analyzing player behavior after wins, losses, and ties. Predict the player's next move and give counter strategy based on the Behavioral probabilities.
+
+**Behavioral Probabilities P(Change/not change | Win/Loss/Tie):**
+* P(not change | Win) = 0.70
+* P(Change | Win) = 0.30
+* P(not change | Loss) = 0.25
+* P(Change | Loss) = 0.75
+* P(not change | Tie) = 0.50
+* P(Change | Tie) = 0.50
+
+**Input Provided by User:**
+* Player's Last Outcome: [Win/Loss/Tie]
+* Player's Last Move: [Rock/Paper/Scissors]
+
+**Your Task:**
+1. Based on the Player's Last Outcome, determine the **Predicted Behavior** by comparing P(not change | Win/Loss/Tie) and P(Change | Win/Loss/Tie).
+2. Determine the **Player's Predicted Next Move**:
+   * If Predicted Behavior is "not change", predict the same move as Player's Last Move.
+   * If Predicted Behavior is "Change", predict a move different from Player's Last Move (randomly select between the two remaining options with equal probability).
+3. Recommend the **AI Counter Move** that beats the predicted player move:
+   * Paper beats Rock
+   * Rock beats Scissors
+   * Scissors beats Paper
+
+**Output Format:**
+Predicted Behavior: [not change/Change] (Based on P(not change|Outcome)=[Prob], P(Change|Outcome)=[Prob])
+Prediction Logic: [Brief explanation of your reasoning]
+Predicted Player Move: [Rock/Paper/Scissors]
+Recommended AI Counter: [Rock/Paper/Scissors]
+"""
+
 # --- Default Input Values ---
 DEFAULT_PLAYER_STATS = "Rock: 40%\nPaper: 30%\nScissors: 30%"
+DEFAULT_PLAYER_BEHAVIOR = "Player's Last Outcome: Win\nPlayer's Last Move: Rock"
 
 # --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(f"# {MODEL_ID} - RPS Strategy Tester")
-    gr.Markdown("Test model advice using either Frequency Stats OR Short-Term (Markov) Predictions.")
+    gr.Markdown("Test model advice using Frequency Stats, Markov Predictions, or Win/Loss/Tie Behavior Analysis.")
 
-    # Mode Selector
+    # Mode Selector - now with three options
     analysis_mode_selector = gr.Radio(
         label="Select Analysis Mode",
-        choices=["Frequency Only", "Markov Prediction Only"],
+        choices=["Frequency Only", "Markov Prediction Only", "Behavior Analysis"],
         value="Frequency Only" # Default mode
     )
 
@@ -203,6 +241,14 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         gr.Markdown("### Markov Prediction Analysis Inputs")
         gr.Markdown("*Use the System Prompt field to directly input your Markov analysis instructions.*")
 
+    # New behavior analysis inputs
+    with gr.Group(visible=False) as behavior_inputs:
+        gr.Markdown("### Win/Loss/Tie Behavior Analysis Inputs")
+        player_behavior_input = gr.Textbox(
+            label="Player's Last Outcome and Move", value=DEFAULT_PLAYER_BEHAVIOR, lines=4,
+            info="Enter the last outcome (Win/Loss/Tie) and move (Rock/Paper/Scissors)."
+        )
+
     # General Inputs / Parameters / Outputs
     with gr.Row():
         with gr.Column():
@@ -243,18 +289,28 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             return {
                 frequency_inputs: gr.update(visible=True),
                 markov_inputs: gr.update(visible=False),
+                behavior_inputs: gr.update(visible=False),
                 system_prompt_input: gr.update(value=DEFAULT_SYSTEM_PROMPT_FREQ) # Load Frequency prompt
             }
         elif mode == "Markov Prediction Only":
             return {
                 frequency_inputs: gr.update(visible=False),
                 markov_inputs: gr.update(visible=True),
+                behavior_inputs: gr.update(visible=False),
                 system_prompt_input: gr.update(value=DEFAULT_SYSTEM_PROMPT_MARKOV) # Load Markov prompt
             }
+        elif mode == "Behavior Analysis":
+            return {
+                frequency_inputs: gr.update(visible=False),
+                markov_inputs: gr.update(visible=False),
+                behavior_inputs: gr.update(visible=True),
+                system_prompt_input: gr.update(value=DEFAULT_SYSTEM_PROMPT_BEHAVIOR) # Load Behavior prompt
+            }
         else: # Default case
              return {
                 frequency_inputs: gr.update(visible=True),
                 markov_inputs: gr.update(visible=False),
+                behavior_inputs: gr.update(visible=False),
                 system_prompt_input: gr.update(value=DEFAULT_SYSTEM_PROMPT_FREQ)
             }
 
@@ -262,7 +318,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     analysis_mode_selector.change(
         fn=update_ui_visibility_and_prompt, # Use the combined update function
         inputs=analysis_mode_selector,
-        outputs=[frequency_inputs, markov_inputs, system_prompt_input] # Components to update
+        outputs=[frequency_inputs, markov_inputs, behavior_inputs, system_prompt_input] # Components to update
     )
 
     # Handle button click - Pass the single visible system prompt
@@ -271,6 +327,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         inputs=[
             analysis_mode_selector,
             player_stats_input,
+            player_behavior_input,
             system_prompt_input, # Pass the visible system prompt textbox
             max_length_slider,
             temperature_slider,
@@ -280,7 +337,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             final_prompt_display, response_display,
             time_output, tokens_output
         ],
-        api_name="generate_rps_selectable_analysis_v3" # Updated api_name
+        api_name="generate_rps_selectable_analysis_v4" # Updated api_name
     )
 
 # --- Launch the demo ---