Refactored main.py

gradio_ui.py

@@ -1,6 +1,9 @@
 import gradio as gr
 import yaml
 import os
+from datetime import datetime
+
+from googletrans import Translator
 
 from core.codex_informer import CodexInformer
 from core.eil_processor import EILProcessor
@@ -8,35 +11,73 @@ from core.esil_inference import ESILInference
 from core.eris_reasoner import ERISReasoner
 from core.fec_controller import FECController
 
-# Initialize MEC Core Components
+# 1️⃣ --- SETUP & BRANDING ---
 codex_informer = CodexInformer()
 eil_processor = EILProcessor(codex_informer)
 esil_formatter = ESILInference(codex_informer)
 eris_engine = ERISReasoner()
 fec_controller = FECController()
 
-# Load Response Strategies YAML
+logo_path = "D:\EmpathyEthics\MEC docs\GIT\mec-mvp\assets\Transparent_logo_40.webp"  # Update if needed
+
 config_path = os.path.join("config", "response_strategies.yaml")
 with open(config_path, 'r', encoding='utf-8') as f:
     response_strategies = yaml.safe_load(f).get('response_strategies', {})
 
-def process_input(user_text):
+LANGS = {
+    "English": "en",
+    "Spanish": "es",
+    "French": "fr",
+    "Chinese (Simplified)": "zh-cn",
+    "German": "de"
+}
+DEFAULT_LANG = "English"
+DEFAULT_TONE = "Empathic (Default)"
+TONES = [
+    "Empathic (Default)",
+    "Direct",
+    "Gentle",
+    "Encouraging",
+    "Reflective"
+]
+
+translator = Translator()
+
+def translate_text(text, target_lang_code):
+    if target_lang_code == "en":
+        return text
     try:
-        print(f"[DEBUG] Received user input: {user_text!r}")
+        result = translator.translate(text, dest=target_lang_code)
+        return result.text
+    except Exception:
+        return f"[Translation Error] {text}"
+
+# 2️⃣ --- PIPELINE ---
+def process_input(user_text, selected_tone, selected_language):
+    trace = []
+    try:
+        trace.append(f"[DEBUG] Input Text: {user_text!r}")
+        trace.append(f"[DEBUG] Selected Tone: {selected_tone}")
+        trace.append(f"[DEBUG] Selected Language: {selected_language}")
+
+        # Optionally translate input to English if not already (for model)
+        lang_code = LANGS.get(selected_language, "en")
+        user_text_en = translate_text(user_text, "en") if lang_code != "en" else user_text
+        if lang_code != "en":
+            trace.append(f"[DEBUG] Translated input to English: {user_text_en}")
 
         # Step 1: EIL Processing
-        eil_result = eil_processor.infer_emotion(user_text)
-        print(f"[DEBUG] EIL Result: {eil_result}")
+        eil_result = eil_processor.infer_emotion(user_text_en)
+        trace.append(f"[DEBUG] EIL Result: {eil_result}")
 
         # Step 2: ESIL Formatting
         esil_packet = esil_formatter.infer_esil(eil_result)
-        print(f"[DEBUG] ESIL Packet: {esil_packet}")
+        trace.append(f"[DEBUG] ESIL Packet: {esil_packet}")
 
         # Step 3: ERIS Reasoning
         eris_result = eris_engine.reason_emotion_state(esil_packet)
-        print(f"[DEBUG] ERIS Result: {eris_result}")
+        trace.append(f"[DEBUG] ERIS Result: {eris_result}")
 
-        # Pull out your primary_emotion_code (case-agnostic)
         fam_code = (
             eris_result.get('primary_emotion_code') or
             eris_result.get('Primary Emotion Code') or
@@ -45,6 +86,16 @@ def process_input(user_text):
         if not fam_code:
             raise KeyError("`primary_emotion_code` missing in ERIS result")
 
+        # Pass tone through to Fusion Engine
+        final_uesp = {
+            'emotion_family': eris_result.get('emotion_family', fam_code),
+            'Primary Emotion Code': fam_code,
+            'response_tone': selected_tone,  # This is new!
+            **eris_result
+        }
+        fusion_prompt = fec_controller.generate_prompt(final_uesp)
+        trace.append(f"[DEBUG] Fusion Prompt Generated: {fusion_prompt}")
+
         # Lookup response strategy
         rs = response_strategies.get(fam_code, {})
         rsm_code        = rs.get('rsm_code', 'RSM-UNKNOWN')
@@ -57,44 +108,99 @@ def process_input(user_text):
             f"{sample_response}"
         )
 
-        # Step 4: Fusion Engine – ensure keys match what FECController expects
-        final_uesp = {
-            # FEC expects these keys:
-            'emotion_family': eris_result.get('emotion_family', fam_code),
-            'Primary Emotion Code': fam_code,
-            **eris_result  # Carry over everything else, in case
-        }
-
-        fusion_prompt = fec_controller.generate_prompt(final_uesp)
-        print("[DEBUG] Fusion Prompt Generated")
-
-        return fusion_prompt, simulated_output
-
+        # Optionally translate output back to user language
+        if lang_code != "en":
+            fusion_prompt = translate_text(fusion_prompt, lang_code)
+            simulated_output = translate_text(simulated_output, lang_code)
+
+        # 3️⃣ --- EMPATHY REPORT GENERATION ---
+        report_contents = (
+            f"Empathy Report - MEC MVP\n"
+            f"Date: {datetime.now().isoformat(timespec='seconds')}\n"
+            f"Input: {user_text}\n"
+            f"Tone: {selected_tone}\n"
+            f"Language: {selected_language}\n"
+            f"\n"
+            f"Primary Emotion: {fam_code}\n"
+            f"Blend: {eris_result.get('blend')}\n"
+            f"Arc: {eris_result.get('arc')}\n"
+            f"Resonance: {eris_result.get('resonance')}\n"
+            f"\n"
+            f"Fusion Prompt:\n{fusion_prompt}\n"
+            f"\n"
+            f"Simulated Empathic Response:\n{simulated_output}\n"
+        )
+        report_file = f"/tmp/mec_empathy_report_{datetime.now().strftime('%Y%m%d%H%M%S')}.txt"
+        with open(report_file, "w", encoding='utf-8') as f:
+            f.write(report_contents)
+
+        return (
+            fusion_prompt,
+            simulated_output,
+            "\n".join(trace),
+            report_file
+        )
     except Exception as e:
-        print(f"[ERROR] Exception in process_input: {e}")
-        return "[ERROR: Unable to process input]", f"An error occurred: {e}"
+        trace.append(f"[ERROR] Exception in process_input: {e}")
+        return (
+            "[ERROR: Unable to process input]",
+            f"An error occurred: {e}",
+            "\n".join(trace),
+            None
+        )
 
-def launch_ui():
-    with gr.Blocks(title="MEC MVP – Empathic UI") as demo:
-        gr.Markdown("# Master Emotional Core (MEC) MVP UI\nEmpathy-first AI. Built to protect.")
+# 4️⃣ --- UI LAYOUT ---
+with gr.Blocks(title="MEC MVP – Empathic UI", theme="soft", css="#logo-img {margin-bottom: -30px;}") as demo:
+    # Branding: Logo & Title
+    with gr.Row():
+        gr.Image(value=logo_path, label="", show_label=False, height=100, elem_id="logo-img")
+        gr.Markdown("## Master Emotional Core (MEC) MVP UI\nEmpathy-first AI. Built to protect.")
+
+    # Guided Onboarding
+    gr.Markdown(
+        "Welcome to the MEC Empathy MVP! Enter a story, thought, or feeling below and select a tone or language for the response. "
+        "You can expand the Emotional Reasoning Trace below to see how the AI made its decision. "
+        "Download your empathy report after each run for further review or research."
+    )
+
+    with gr.Row():
+        user_input = gr.Textbox(
+            label="Enter your message or story:",
+            placeholder="Type here... (Long-form, multi-sentence, or single phrase)",
+            lines=5,
+            info="Try stories, complex feelings, or emotional blends!"
+        )
 
-        with gr.Row():
-            user_input = gr.Textbox(label="Enter your message:", placeholder="Type here...", lines=3)
+    with gr.Row():
+        selected_tone = gr.Dropdown(
+            choices=TONES,
+            value=DEFAULT_TONE,
+            label="Select Empathic Response Tone",
+            info="Choose how the AI should frame its empathic response."
+        )
+        selected_language = gr.Dropdown(
+            choices=list(LANGS.keys()),
+            value=DEFAULT_LANG,
+            label="Language",
+            info="Choose your preferred language for input/output."
+        )
 
-        with gr.Row():
-            submit_button = gr.Button("Process Input")
+    with gr.Row():
+        submit_button = gr.Button("Process Input")
+        download_report_btn = gr.File(label="Download Empathy Report", visible=True)
 
-        with gr.Row():
-            fusion_prompt_display = gr.Textbox(label="Fusion Prompt", lines=6, interactive=False)
-            empathic_response_display = gr.Textbox(label="Simulated Empathic Response", lines=8, interactive=False)
+    with gr.Row():
+        fusion_prompt_display = gr.Textbox(label="Fusion Prompt", lines=5, interactive=False)
+        empathic_response_display = gr.Textbox(label="Simulated Empathic Response", lines=7, interactive=False)
 
-        submit_button.click(
-            fn=process_input,
-            inputs=[user_input],
-            outputs=[fusion_prompt_display, empathic_response_display]
-        )
+    with gr.Accordion("Show Emotional Reasoning Trace (Technical)", open=False):
+        reasoning_trace = gr.Textbox(label="Debug/Trace", lines=15, interactive=False)
 
-    demo.launch()
+    # Submit logic
+    submit_button.click(
+        fn=process_input,
+        inputs=[user_input, selected_tone, selected_language],
+        outputs=[fusion_prompt_display, empathic_response_display, reasoning_trace, download_report_btn]
+    )
 
-if __name__ == "__main__":
-    launch_ui()
+demo.launch()

main.py

@@ -1,3 +1,4 @@
+from core.codex_informer import CodexInformer
 from core.eil_processor import EILProcessor
 from core.esil_inference import ESILInference
 from core.eris_reasoner import ERISReasoner
@@ -13,15 +14,16 @@ def run_pipeline(user_input_text, force_hei=False):
     with open('config/response_strategies.yaml', 'r', encoding='utf-8') as f:
         response_strategies = yaml.safe_load(f)['response_strategies']
 
-    # 1️⃣ EIL Processor (handles both normalization and emotion processing)
-    eil = EILProcessor()
+    # 1️⃣ CodexInformer and EIL Processor (handles both normalization and emotion processing)
+    codex_informer = CodexInformer()
+    eil = EILProcessor(codex_informer)
 
     # Run emotion inference
     eil_packet = eil.infer_emotion(user_input_text)
     print(f"[Main] EIL Packet Output: {eil_packet}")
 
     # 2️⃣ ESIL Inference
-    esil = ESILInference()
+    esil = ESILInference(codex_informer)
     esil_packet = esil.infer_esil(eil_packet)
 
     # 3️⃣ Forced HEI Mode: Ensure it forces the low confidence path if True
@@ -45,7 +47,7 @@ def run_pipeline(user_input_text, force_hei=False):
         print(f"[Main] Final Fusion Prompt:\n{fusion_prompt}")
 
         # 6️⃣ Simulated Empathic Response
-        fam_code = final_uesp['primary_emotion_code']
+        fam_code = final_uesp.get('primary_emotion_code') or final_uesp.get('Primary Emotion Code')
         rsm_code = response_strategies.get(fam_code, {}).get('rsm_code', 'RSM-UNKNOWN')
         strategy_name = response_strategies.get(fam_code, {}).get('strategy', 'Strategy not defined')
         sample_response = response_strategies.get(fam_code, {}).get('sample_response', 'No response available')
@@ -76,7 +78,7 @@ def run_pipeline(user_input_text, force_hei=False):
         print(f"[Main] Final Fusion Prompt:\n{fusion_prompt}")
 
         # 8️⃣ Simulated Empathic Response
-        fam_code = final_uesp['primary_emotion_code']
+        fam_code = final_uesp.get('primary_emotion_code') or final_uesp.get('Primary Emotion Code')
         rsm_code = response_strategies.get(fam_code, {}).get('rsm_code', 'RSM-UNKNOWN')
         strategy_name = response_strategies.get(fam_code, {}).get('strategy', 'Strategy not defined')
         sample_response = response_strategies.get(fam_code, {}).get('sample_response', 'No response available')