Update app.py

app.py

@@ -18,7 +18,6 @@ print("Model loaded successfully.")
 
 
 # --- Generation Function (Returns response and token count) ---
-# No changes needed here
 def generate_response(messages, max_length=512, temperature=0.7, top_p=0.9):
     """Generate a response and return it along with the number of generated tokens."""
     num_generated_tokens = 0
@@ -59,10 +58,7 @@ def generate_response(messages, max_length=512, temperature=0.7, top_p=0.9):
 def process_input(
     analysis_mode, # Mode selector
     player_stats,
-    player_last_move,
-    markov_prediction_text,
     system_prompt, # Single system prompt from UI
-    user_query,
     max_length,
     temperature,
     top_p
@@ -72,12 +68,9 @@ def process_input(
 
     # Construct user content based on mode
     if analysis_mode == "Frequency Only":
-        user_content = f"Player Move Frequency Stats (Long-Term):\n{player_stats}\n\n"
-        user_content += f"User Query:\n{user_query}"
+        user_content = f"Player Move Frequency Stats (Long-Term):\n{player_stats}"
     elif analysis_mode == "Markov Prediction Only":
-        user_content = f"Player's Last Move:\n{player_last_move}\n\n"
-        user_content += f"Predicted Next Move (Short-Term Markov Analysis):\n{markov_prediction_text}\n\n"
-        user_content += f"User Query:\n{user_query}"
+        user_content = "Analyze based on the system prompt instructions."
     else:
         return "Invalid analysis mode selected.", "", "0 seconds", 0
 
@@ -133,29 +126,11 @@ Step 4: State Final Recommendation.
 Base your analysis strictly on the provided frequencies and the stated RPS rules.
 """
 
-# *** UPDATED Markov System Prompt v2 ***
-DEFAULT_SYSTEM_PROMPT_MARKOV = """You are an RPS assistant using short-term pattern analysis (Markov prediction).
-Your ONLY task is to recommend the AI move that beats the player's PREDICTED next move. Accuracy is critical.
-
-Input Information Provided:
-- Player's Predicted Next Move (from Markov analysis): [This is the key input!]
-
-Instructions:
-1.  **Identify Prediction:** State the player's PREDICTED next move (Rock, Paper, or Scissors) based *only* on the 'Predicted Next Move' input.
-2.  **Find Counter:** Apply the RPS rules (Paper beats Rock, Rock beats Scissors, Scissors beats Paper). Determine the single move that correctly beats the PREDICTED move from Step 1. State *only* the name of this counter move. Double-check the rules.
-3.  **Recommend:** Clearly state the counter move found in Step 2 as the AI's recommended move.
-
-Example Output Format:
-1. Predicted Player Move: [Predicted move name]
-2. Counter Move: [Counter move name]
-3. Recommendation: Play [Counter move name].
-"""
+# Empty Markov system prompt as requested
+DEFAULT_SYSTEM_PROMPT_MARKOV = ""
 
 # --- Default Input Values ---
 DEFAULT_PLAYER_STATS = "Rock: 40%\nPaper: 30%\nScissors: 30%"
-DEFAULT_PLAYER_LAST_MOVE = "Rock"
-DEFAULT_MARKOV_PREDICTION = "Based on the last move (Rock), the player's most likely next move is Paper (60% probability)."
-DEFAULT_USER_QUERY = "Based on the provided information for the selected analysis mode, what single move should the AI make next? Explain your reasoning step-by-step as instructed."
 
 # --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
@@ -186,29 +161,16 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     with gr.Group(visible=False) as markov_inputs: # Hidden by default
         gr.Markdown("### Markov Prediction Analysis Inputs")
-        player_last_move_input = gr.Dropdown(
-            label="Player's Last Move", choices=["Rock", "Paper", "Scissors"], value=DEFAULT_PLAYER_LAST_MOVE,
-            info="The player's most recent actual move."
-        )
-        markov_prediction_input = gr.Textbox(
-            label="Predicted Next Move (Short-Term Markov Analysis)", value=DEFAULT_MARKOV_PREDICTION, lines=3,
-            info="Provide the pre-calculated prediction based on the last move (e.g., 'Player likely plays Paper (60%)')."
-        )
+        gr.Markdown("*Use the System Prompt field to directly input your Markov analysis instructions.*")
 
     # General Inputs / Parameters / Outputs
     with gr.Row():
-         with gr.Column(scale=2):
-             user_query_input = gr.Textbox(
-                 label="Your Query / Instruction", value=DEFAULT_USER_QUERY, lines=3,
-                 info="Ask the specific question based on the selected mode's analysis."
-             )
-         with gr.Column(scale=1):
+        with gr.Column():
             gr.Markdown("#### Generation Parameters")
             max_length_slider = gr.Slider(minimum=50, maximum=1024, value=300, step=16, label="Max New Tokens")
             temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.4, step=0.05, label="Temperature")
             top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top P")
 
-
     submit_btn = gr.Button("Generate Response", variant="primary")
 
     with gr.Row():
@@ -247,7 +209,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             return {
                 frequency_inputs: gr.update(visible=False),
                 markov_inputs: gr.update(visible=True),
-                system_prompt_input: gr.update(value=DEFAULT_SYSTEM_PROMPT_MARKOV) # Load Markov prompt
+                system_prompt_input: gr.update(value=DEFAULT_SYSTEM_PROMPT_MARKOV) # Load empty Markov prompt
             }
         else: # Default case
              return {
@@ -269,10 +231,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         inputs=[
             analysis_mode_selector,
             player_stats_input,
-            player_last_move_input,
-            markov_prediction_input,
             system_prompt_input, # Pass the visible system prompt textbox
-            user_query_input,
             max_length_slider,
             temperature_slider,
             top_p_slider
@@ -281,9 +240,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             final_prompt_display, response_display,
             time_output, tokens_output
         ],
-        api_name="generate_rps_selectable_analysis_v2" # Updated api_name
+        api_name="generate_rps_selectable_analysis_v3" # Updated api_name
     )
 
 # --- Launch the demo ---
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()