Upload app.py

app.py

@@ -22,12 +22,11 @@ except TypeError:
     api_key_found = False
 
 print("Loading Whisper for transcription...")
-whisper_model = whisper.load_model("base", device="cpu") 
+whisper_model = whisper.load_model("base", device="cpu")
 print("Whisper model loaded.")
 
 
 # --- 1. DEFINICIÓN DE PROMPTS PARA LA IA ---
-# (Estos prompts se mantienen igual que en tu versión)
 
 CONVERSATION_SYSTEM_PROMPT = """
 You are a friendly and encouraging English language tutor named Alex. 
@@ -61,20 +60,27 @@ JSON Output Structure:
 }
 """
 
-# --- 2. FUNCIONES LÓGICAS (Con ajustes en 'chat_interaction') ---
+# --- 2. FUNCIONES LÓGICAS ---
 
 def extract_word_level_features(audio_path):
-    # ... (esta función se mantiene igual)
-    pass
+    try:
+        y, sr = librosa.load(audio_path, sr=16000)
+        result = whisper_model.transcribe(audio_path, word_timestamps=True, fp16=False)
+        if not result["segments"] or 'words' not in result["segments"][0]: return []
+        word_segments = result["segments"][0]["words"]
+        features_list = []
+        for segment in word_segments:
+            start_sample = int(segment['start'] * sr); end_sample = int(segment['end'] * sr)
+            word_audio = y[start_sample:end_sample]
+            rms_energy = np.mean(librosa.feature.rms(y=word_audio)) if len(word_audio) > 0 else 0
+            features_list.append({"word": segment['word'].strip(), "start": round(segment['start'], 2), "end": round(segment['end'], 2), "energy": round(float(rms_energy), 4)})
+        return features_list
+    except Exception as e:
+        print(f"Error during feature extraction: {e}"); return []
 
 def chat_interaction(audio_input, history_state):
     if not api_key_found: raise gr.Error("OpenAI API key not found.")
-    if audio_input is None:
-        user_turns = len(history_state[1:]) // 2 if history_state else 0
-        responses_remaining = 5 - user_turns
-        # Muestra el estado actual sin hacer nada si no hay audio
-        chat_display = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(1, len(history_state), 2)]
-        return chat_display, history_state, f"Responses remaining: {responses_remaining}", gr.update(visible=False), gr.update(visible=False)
+    if audio_input is None: return history_state, history_state, gr.Markdown(visible=False), gr.Markdown(visible=False)
 
     sr, y = audio_input; temp_audio_path = "temp_audio_chat.wav"; sf.write(temp_audio_path, y, sr)
     user_text = client.audio.transcriptions.create(model="whisper-1", file=open(temp_audio_path, "rb")).text
@@ -84,17 +90,16 @@ def chat_interaction(audio_input, history_state):
 
     history_state.append({"role": "user", "content": user_text})
     
-    user_turns = (len(history_state) - 1) // 2
-    responses_remaining = 5 - user_turns
-    
-    if user_turns < 5:
+    chat_display = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(1, len(history_state), 2)]
+
+    if len(history_state) < 10:
         response = client.chat.completions.create(model="gpt-4o", messages=history_state, temperature=0.7)
         ai_response = response.choices[0].message.content
         history_state.append({"role": "assistant", "content": ai_response})
-        chat_display = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(1, len(history_state), 2)]
-        return chat_display, history_state, f"Responses remaining: {responses_remaining}", gr.update(visible=False), gr.update(visible=False)
-    else: # Turno 5: generar evaluación
-        print("Generating final evaluation...")
+        chat_display.append((user_text, ai_response))
+        return chat_display, history_state, gr.Markdown(visible=False), gr.Markdown(visible=False)
+    else:
+        print("Generating final evaluation...");
         final_messages = [{"role": "system", "content": FINAL_EVALUATION_SYSTEM_PROMPT}] + history_state[1:]
         response = client.chat.completions.create(model="gpt-4o", response_format={"type": "json_object"}, messages=final_messages)
         try:
@@ -103,68 +108,55 @@ def chat_interaction(audio_input, history_state):
             for item in fb_en.get('word_by_word_feedback', []): md_en += f"- **{item['word']}**: {item['feedback']}\n"
             fb_es = result.get('feedback_es', {}); md_es = f"## Reporte Final (Nivel MCERL: {result.get('cefr_level', 'N/A')})\n### Fortalezas\n{fb_es.get('fortalezas', '')}\n### Áreas a Mejorar\n{fb_es.get('areas_a_mejorar', '')}\n### Retroalimentación por Palabra\n"
             for item in fb_es.get('feedback_por_palabra', []): md_es += f"- **{item['palabra']}**: {item['feedback']}\n"
-            
-            chat_display = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(1, len(history_state)-1, 2)]
-            chat_display.append((user_text, "Thank you! Your final report is now available on the right."))
-            
-            # --- CAMBIO CLAVE: Reiniciamos el historial para la siguiente conversación ---
-            return chat_display, [], "Conversation finished!", gr.update(value=md_en, visible=True), gr.update(value=md_es, visible=True)
-        except Exception as e:
-            print(f"Error parsing final report: {e}")
-            chat_display = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(1, len(history_state)-1, 2)]
-            return chat_display, [], "Error!", gr.update(value="Error generating report.", visible=True), gr.update(visible=False)
+            chat_display.append((user_text, "Thank you for the conversation! Here is your final report below."))
+            return chat_display, [], gr.Markdown(value=md_en, visible=True), gr.Markdown(value=md_es, visible=True)
+        except (json.JSONDecodeError, KeyError) as e:
+            print(f"Error parsing final report: {e}"); return [], [], gr.Markdown(value="Error generating report.", visible=True), gr.Markdown(visible=False)
 
 def run_sentence_evaluation(audio_input, reference_transcript):
-    # ... (esta función se mantiene igual)
-    pass
-
-# --- 3. INTERFAZ DE GRADIO CON PESTAÑAS (Con ajustes en la Pestaña 1) ---
+    if not api_key_found: raise gr.Error("OpenAI API key not found.")
+    if audio_input is None or not reference_transcript:
+        return 0, "N/A", "Please provide both an audio file and the reference text.", ""
+    sr, y = audio_input; temp_audio_path = "temp_audio_sentence.wav"; sf.write(temp_audio_path, y, sr)
+    word_features = extract_word_level_features(temp_audio_path)
+    if not word_features:
+        return 0, "N/A", "Could not process the audio.", ""
+    prompt_data = {"reference_transcript": reference_transcript, "spoken_words": word_features}
+    print("Sending detailed data to GPT-4o for sentence analysis...")
+    response = client.chat.completions.create(model="gpt-4o", response_format={"type": "json_object"}, messages=[{"role": "system", "content": SENTENCE_EVALUATION_SYSTEM_PROMPT}, {"role": "user", "content": json.dumps(prompt_data)}])
+    try:
+        result = json.loads(response.choices[0].message.content)
+        holistic_feedback_md = f"### Strengths\n{result['holistic_feedback']['strengths']}\n\n### Areas for Improvement\n{result['holistic_feedback']['areas_for_improvement']}"
+        word_analysis_list = result['word_by_word_analysis']
+        md_table = "| Reference Word | Spoken Word | Score | Feedback (EN) | Feedback (ES) | Reference Audio |\n| :--- | :--- | :---: | :--- | :--- | :---: |\n"
+        for index, item in enumerate(word_analysis_list):
+            word_to_speak = item['reference_word']
+            try:
+                tts = gTTS(text=word_to_speak, lang='en'); mp3_fp = io.BytesIO(); tts.write_to_fp(mp3_fp); mp3_fp.seek(0)
+                audio_base64 = base64.b64encode(mp3_fp.read()).decode('utf-8')
+                audio_player = f'<audio src="data:audio/mpeg;base64,{audio_base64}" controls></audio>'
+            except Exception as e:
+                print(f"Error al generar TTS para '{word_to_speak}': {e}"); audio_player = "Error"
+            md_table += (f"| **{item['reference_word']}** | {item['spoken_word']} | {item['word_score_100']} | {item['feedback_en']} | {item['feedback_es']} | {audio_player} |\n")
+        return (result.get("overall_score_100", 0), result.get("cefr_level", "N/A"), holistic_feedback_md, md_table)
+    except (json.JSONDecodeError, KeyError) as e:
+        print(f"Error processing API response: {e}"); error_msg = "The API response was not in the expected format."
+        return 0, "Error", error_msg, ""
+
+# --- 3. INTERFAZ DE GRADIO CON PESTAÑAS ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🇬🇧 AI English Speaking Practice & Assessment")
-    
     with gr.Tabs():
-        # --- PESTAÑA 1: CHAT AI (CON MEJORAS) ---
+        # --- PESTAÑA 1: CHAT AI ---
         with gr.TabItem("Práctica Conversacional (Chat AI)"):
             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", height=500)
                     audio_in_chat = gr.Audio(sources=["microphone"], type="numpy", label="Record your response")
-                    with gr.Row():
-                        counter_out = gr.Textbox(value="Responses remaining: 5", label="Conversation Progress", interactive=False)
-                        # --- CAMBIO: Botón para reiniciar la conversación completa ---
-                        new_conversation_btn = gr.Button("New Conversation")
-                        
                 with gr.Column(scale=1):
-                    gr.Markdown("### Final Report")
-                    feedback_en_out = gr.Markdown(label="English Feedback", visible=False)
-                    feedback_es_out = gr.Markdown(label="Retroalimentación en Español", visible=False)
-
+                    gr.Markdown("### Final Report"); feedback_en_out = gr.Markdown(label="English Feedback", visible=False); feedback_es_out = gr.Markdown(label="Retroalimentación en Español", visible=False)
             history = gr.State([])
-            
-            # Función para borrar el audio después de enviarlo (no es un botón, es una acción)
-            def clear_audio_input():
-                return None
-            
-            # Función para reiniciar toda la conversación
-            def clear_conversation():
-                return [], [(None, "Hi there! I'm Alex. How are you doing today?")], "Responses remaining: 5", gr.update(visible=False), gr.update(visible=False), None
-            
-            # --- CAMBIO: Se renombra el botón y se conecta a la nueva función de reinicio ---
-            new_conversation_btn.click(
-                fn=clear_conversation, 
-                inputs=[], 
-                outputs=[history, chatbot, counter_out, feedback_en_out, feedback_es_out, audio_in_chat]
-            )
-            
-            audio_in_chat.stop_recording(
-                fn=chat_interaction,
-                inputs=[audio_in_chat, history],
-                outputs=[chatbot, history, counter_out, feedback_en_out, feedback_es_out]
-            ).then(
-                fn=clear_audio_input,
-                inputs=[],
-                outputs=[audio_in_chat]
-            )
+            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 ---
         with gr.TabItem("Evaluación por Frase"):
@@ -188,7 +180,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             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])
 
-
 if __name__ == "__main__":
-    if not api_key_found: print("\nFATAL: OpenAI API key not found.")
+    if not api_key_found: print("\nFATAL: OpenAI API key not found. Please set the OPENAI_API_KEY environment variable.")
     else: demo.launch(debug=True)