Upload app.py

app.py

@@ -137,12 +137,15 @@ def run_sentence_evaluation(audio_input, reference_transcript):
         return 0, "Error", error_msg, None
 
 
-# --- 3. INTERFAZ DE GRADIO CON PESTAÑAS ---
+# --- 3. INTERFAZ DE GRADIO CON PESTAÑAS (Con la tabla corregida) ---
+
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🇬🇧 AI English Speaking Practice & Assessment")
+    
     with gr.Tabs():
-        # --- PESTAÑA 1: CHAT AI ---
+        # --- PESTAÑA 1: CHAT AI (sin cambios) ---
         with gr.TabItem("Práctica Conversacional (Chat AI)"):
+            # ... (todo el código de la interfaz del chatbot se mantiene igual)
             with gr.Row():
                 with gr.Column(scale=2):
                     chatbot = gr.Chatbot(value=[(None, "Hi there! I'm Alex. How are you doing today?")], label="Conversation with your AI Tutor")
@@ -152,14 +155,14 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             history = gr.State([])
             audio_in_chat.stop_recording(fn=chat_interaction, inputs=[audio_in_chat, history], outputs=[chatbot, history, feedback_en_out, feedback_es_out])
 
-        # --- PESTAÑA 2: EVALUACIÓN POR FRASE ---
+        # --- PESTAÑA 2: EVALUACIÓN POR FRASE (Con la tabla corregida) ---
         with gr.TabItem("Evaluación por Frase"):
             TONGUE_TWISTERS = ["Peter Piper picked a peck of pickled peppers.", "She sells seashells by the seashore.", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", "Betty Botter bought some butter but she said the butter’s bitter.", "A proper copper coffee pot."]
             gr.Markdown("Choose a tongue twister or write your own sentence. Record yourself, and our AI examiner will provide a detailed diagnostic report.")
             tongue_twister_selector = gr.Dropdown(choices=TONGUE_TWISTERS, label="Or Choose a Tongue Twister to Practice")
+            
             with gr.Row():
                 with gr.Column(scale=1):
-                    # --- CORRECCIÓN: Parámetros completos en lugar de '...' ---
                     audio_in_sentence = gr.Audio(sources=["microphone"], type="numpy", label="1. Record Your Voice")
                     text_in_sentence = gr.Textbox(lines=3, label="2. Reference Sentence", value=TONGUE_TWISTERS[0])
                     submit_btn_sentence = gr.Button("Get Assessment", variant="primary")
@@ -169,11 +172,26 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                         score_out_sentence = gr.Number(label="Overall Score (0-100)", interactive=False)
                         level_out_sentence = gr.Textbox(label="Estimated CEFR Level", interactive=False)
                     holistic_feedback_out_sentence = gr.Markdown(label="Examiner's Feedback")
+            
             gr.Markdown("--- \n ### Detailed Word-by-Word Analysis")
-            word_analysis_out_sentence = gr.DataFrame(headers=["Reference Word", "Spoken Word", "Score (0-100)", "Feedback (English)", "Feedback (Español)", "Reference Audio"], label="Phonetic Breakdown", wrap=True)
+            
+            # --- AJUSTE CLAVE: Especificamos el tipo de dato para cada columna ---
+            word_analysis_out_sentence = gr.DataFrame(
+                headers=["Reference Word", "Spoken Word", "Score (0-100)", "Feedback (English)", "Feedback (Español)", "Reference Audio"],
+                # Le decimos a Gradio que la última columna es de tipo 'audio'
+                datatype=["str", "str", "number", "str", "str", "audio"],
+                label="Phonetic Breakdown", 
+                wrap=True
+            )
+
             def update_text(choice): return gr.Textbox(value=choice)
             tongue_twister_selector.change(fn=update_text, inputs=tongue_twister_selector, outputs=text_in_sentence)
-            submit_btn_sentence.click(fn=run_sentence_evaluation, inputs=[audio_in_sentence, text_in_sentence], outputs=[score_out_sentence, level_out_sentence, holistic_feedback_out_sentence, word_analysis_out_sentence])
+
+            submit_btn_sentence.click(
+                fn=run_sentence_evaluation, 
+                inputs=[audio_in_sentence, text_in_sentence], 
+                outputs=[score_out_sentence, level_out_sentence, holistic_feedback_out_sentence, word_analysis_out_sentence]
+            )
 
 if __name__ == "__main__":
     if not api_key_found: print("\nFATAL: OpenAI API key not found.")