Update app.py

app.py

@@ -1,117 +1,125 @@
-import gradio as gr
-import yaml
-import json
-import os
-import traceback
-import matplotlib.pyplot as plt
-import numpy as np
-
-from engine.loader import load_persona
-from engine.drift import apply_stimuli
-from engine.responder import generate_response
-from engine.utils import safe_log
-from engine.logger import log_transcript
-
-# Paths
-persona_dir = "./personas"
-stimuli_path = "./stimuli/events.json"
-error_log_path = "./driftline_errors.log"
-
-
-
-# Load available personas
-def get_persona_choices():
-    return [f for f in os.listdir(persona_dir) if f.endswith(".yml")]
-
-# Load available stimuli
-def get_stimuli_choices():
-    try:
-        with open(stimuli_path, "r") as f:
-            events = json.load(f)
-        return [e["event"] for e in events]
-    except Exception as e:
-        safe_log("Stimuli load error", str(e))
-        return []
-
-# Generate radar chart for traits
-def plot_traits(state):
-    traits = ["innovation", "openness", "risk_tolerance", "peer_influence"]
-    values = [state.get(t, 0.0) for t in traits]
-    angles = np.linspace(0, 2 * np.pi, len(traits), endpoint=False).tolist()
-    values += values[:1]
-    angles += angles[:1]
-
-    fig, ax = plt.subplots(figsize=(4, 4), subplot_kw=dict(polar=True))
-    ax.plot(angles, values, color="blue", linewidth=2)
-    ax.fill(angles, values, color="blue", alpha=0.25)
-    ax.set_xticks(angles[:-1])
-    ax.set_xticklabels(traits)
-    ax.set_yticklabels([])
-    ax.set_title("Dynamic Trait Profile", fontsize=12)
-    fig.tight_layout()
-
-    chart_path = "./trait_chart.png"
-    fig.savefig(chart_path)
-    plt.close(fig)
-    return chart_path
-
-# Main simulation function
-def simulate(prompt, selected_event, selected_persona_file):
-    try:
-        persona_path = os.path.join(persona_dir, selected_persona_file)
-        persona = load_persona(persona_path)
-
-        with open(stimuli_path, "r") as f:
-            events = json.load(f)
-        event = next((e for e in events if e["event"] == selected_event), None)
-
-        if event:
-            persona = apply_stimuli(persona, event)
-
-        response = generate_response(prompt, persona)
-        state_yaml = yaml.dump(persona["dynamic_state"], sort_keys=False)
-        chart_path = plot_traits(persona["dynamic_state"])
-
-        # ? Log transcript here, after response is generated
-        from engine.logger import log_transcript
-        transcript_path = log_transcript(persona, prompt, selected_event, response)
-
-        return response, state_yaml, chart_path
-
-    except Exception as e:
-        error_msg = traceback.format_exc()
-        safe_log("Simulation error", error_msg)
-        return "[ERROR] Simulation failed. Check logs.", "", None
-
-# Gradio UI
-with gr.Blocks(title="Driftline HCP Simulator") as ui:
-    gr.Markdown("## 🧠 Driftline: Adaptive HCP Simulation")
-    gr.Markdown("Simulate how healthcare personas evolve in response to market stimuli.")
-
-    with gr.Row():
-        persona_selector = gr.Dropdown(label="Choose Persona", choices=get_persona_choices(), value="hcp_drift_trailblazer.yml")
-        event_selector = gr.Dropdown(label="Market Stimulus", choices=get_stimuli_choices(), value="FDA_approval")
-
-    prompt = gr.Textbox(label="Interviewer Prompt", lines=2, placeholder="Ask about a new therapy, trial data, or prescribing behavior...")
-
-    with gr.Row():
-        simulate_btn = gr.Button("Run Simulation")
-        clear_btn = gr.Button("Clear")
-
-    response_output = gr.Textbox(label="Simulated HCP Response", lines=6)
-    state_output = gr.Textbox(label="Updated Persona State", lines=10)
-    trait_chart = gr.Image(label="Trait Radar Chart")
-
-    simulate_btn.click(
-        fn=simulate,
-        inputs=[prompt, event_selector, persona_selector],
-        outputs=[response_output, state_output, trait_chart]
-    )
-
-    clear_btn.click(
-        fn=lambda: ("", "", None),
-        inputs=[],
-        outputs=[response_output, state_output, trait_chart]
-    )
-
-ui.launch(server_port=7865, share=True)
+import gradio as gr
+import yaml
+import json
+import os
+import traceback
+import matplotlib.pyplot as plt
+import numpy as np
+
+from engine.loader import load_persona
+from engine.drift import apply_stimuli
+from engine.responder import generate_response
+from engine.utils import safe_log
+from engine.logger import log_transcript
+
+# Paths
+persona_dir = "./personas"
+stimuli_path = "./stimuli/events.json"
+error_log_path = "./driftline_errors.log"
+
+
+
+# Load available personas
+def get_persona_choices():
+    return [f for f in os.listdir(persona_dir) if f.endswith(".yml")]
+
+# Load available stimuli
+def get_stimuli_choices():
+    try:
+        with open(stimuli_path, "r") as f:
+            events = json.load(f)
+        return [e["event"] for e in events]
+    except Exception as e:
+        safe_log("Stimuli load error", str(e))
+        return []
+
+# Generate radar chart for traits
+def plot_traits(state):
+    traits = ["innovation", "openness", "risk_tolerance", "peer_influence"]
+    values = [state.get(t, 0.0) for t in traits]
+    angles = np.linspace(0, 2 * np.pi, len(traits), endpoint=False).tolist()
+    values += values[:1]
+    angles += angles[:1]
+
+    fig, ax = plt.subplots(figsize=(4, 4), subplot_kw=dict(polar=True))
+    ax.plot(angles, values, color="blue", linewidth=2)
+    ax.fill(angles, values, color="blue", alpha=0.25)
+    ax.set_xticks(angles[:-1])
+    ax.set_xticklabels(traits)
+    ax.set_yticklabels([])
+    ax.set_title("Dynamic Trait Profile", fontsize=12)
+    fig.tight_layout()
+
+    chart_path = "./trait_chart.png"
+    fig.savefig(chart_path)
+    plt.close(fig)
+    return chart_path
+
+# Main simulation function
+def simulate(prompt, selected_event, selected_persona_file):
+    try:
+        persona_path = os.path.join(persona_dir, selected_persona_file)
+        persona = load_persona(persona_path)
+
+        with open(stimuli_path, "r") as f:
+            events = json.load(f)
+        event = next((e for e in events if e["event"] == selected_event), None)
+
+        if event:
+            persona = apply_stimuli(persona, event)
+
+        response = generate_response(prompt, persona)
+        state_yaml = yaml.dump(persona["dynamic_state"], sort_keys=False)
+        chart_path = plot_traits(persona["dynamic_state"])
+
+        # ? Log transcript here, after response is generated
+        from engine.logger import log_transcript
+        transcript_path = log_transcript(persona, prompt, selected_event, response)
+
+        return response, state_yaml, chart_path
+
+    except Exception as e:
+        error_msg = traceback.format_exc()
+        safe_log("Simulation error", error_msg)
+        return "[ERROR] Simulation failed. Check logs.", "", None
+
+# Gradio UI
+with gr.Blocks(title="Driftline HCP Simulator") as ui:
+    gr.Markdown("## 🧠 Driftline: Adaptive HCP Simulation")
+    gr.Markdown("Simulate how healthcare personas evolve in response to market stimuli.")
+
+    with gr.Row():
+        persona_files = get_persona_choices()
+        default_persona = persona_files[0] if persona_files else None
+
+        persona_selector = gr.Dropdown(
+        label="Choose Persona",
+        choices=persona_files,
+        value=default_persona,
+        allow_custom_value=False
+)
+        event_selector = gr.Dropdown(label="Market Stimulus", choices=get_stimuli_choices(), value="FDA_approval")
+
+    prompt = gr.Textbox(label="Interviewer Prompt", lines=2, placeholder="Ask about a new therapy, trial data, or prescribing behavior...")
+
+    with gr.Row():
+        simulate_btn = gr.Button("Run Simulation")
+        clear_btn = gr.Button("Clear")
+
+    response_output = gr.Textbox(label="Simulated HCP Response", lines=6)
+    state_output = gr.Textbox(label="Updated Persona State", lines=10)
+    trait_chart = gr.Image(label="Trait Radar Chart")
+
+    simulate_btn.click(
+        fn=simulate,
+        inputs=[prompt, event_selector, persona_selector],
+        outputs=[response_output, state_output, trait_chart]
+    )
+
+    clear_btn.click(
+        fn=lambda: ("", "", None),
+        inputs=[],
+        outputs=[response_output, state_output, trait_chart]
+    )
+
+ui.launch()