Upload app.py

app.py

@@ -22,7 +22,6 @@ print("Loading Whisper for transcription...")
 whisper_model = whisper.load_model("base", device="cpu") 
 print("Whisper model loaded.")
 
-
 # --- 1. DEFINICIÓN DE PROMPTS PARA LA IA ---
 
 # Prompt para mantener la conversación
@@ -34,162 +33,138 @@ A student will speak to you. Your task is to keep a natural, simple conversation
 3. Your entire response must be a single, short paragraph in natural, conversational English. DO NOT use JSON.
 """
 
-# Prompt para la evaluación final
+# Prompt para la evaluación final de la conversación
 FINAL_EVALUATION_SYSTEM_PROMPT = """
 You are an expert English language examiner providing a final report. Analyze the entire conversation history provided.
-
 Your task is to return a single, valid JSON object with the following structure. Do not include any text outside this JSON object.
-
 JSON Output Structure:
 {
   "cefr_level": "string (e.g., A2, B1)",
-  "feedback_en": {
-    "strengths": "string (A paragraph summarizing the student's strong points in pronunciation, vocabulary, and fluency.)",
-    "areas_for_improvement": "string (A paragraph detailing the main patterns of error and what to focus on.)",
-    "word_by_word_feedback": [
-      {"word": "string", "feedback": "string (Specific phonetic or usage feedback.)"}
-    ]
-  },
-  "feedback_es": {
-    "fortalezas": "string (Un párrafo resumiendo los puntos fuertes del estudiante en pronunciación, vocabulario y fluidez.)",
-    "areas_a_mejorar": "string (Un párrafo detallando los patrones de error principales y en qué enfocarse.)",
-    "feedback_por_palabra": [
-      {"palabra": "string", "feedback": "string (Retroalimentación fonética o de uso específica.)"}
-    ]
-  }
+  "feedback_en": { "strengths": "string", "areas_for_improvement": "string", "word_by_word_feedback": [{"word": "string", "feedback": "string"}] },
+  "feedback_es": { "fortalezas": "string", "areas_a_mejorar": "string", "feedback_por_palabra": [{"palabra": "string", "feedback": "string"}] }
 }
 """
 
+# Prompt para la evaluación de una sola frase
+SENTENCE_EVALUATION_SYSTEM_PROMPT = """
+You are an expert English language examiner specializing in phonetics. Your task is to provide a detailed, diagnostic assessment of a student's spoken English based on a reference sentence and detailed word-level audio analysis.
+Input You Will Receive: A JSON object with `reference_transcript` and a list of `spoken_words` with timestamps and energy.
+Your entire response MUST be in English. You must return a single, valid JSON object with the following structure. Do not include any text outside this JSON object.
+JSON Output Structure:
+{
+  "overall_score_100": integer,
+  "cefr_level": "string (A1, A2, B1, B2, C1, or C2)",
+  "holistic_feedback": { "strengths": "string", "areas_for_improvement": "string" },
+  "word_by_word_analysis": [ { "reference_word": "string", "spoken_word": "string", "word_score_100": integer, "correct_ipa": "string", "feedback": "string" } ]
+}
+"""
 
 # --- 2. FUNCIONES LÓGICAS ---
 
-def transcribe_audio(audio_input):
-    """Transcribe el audio usando la API de Whisper de OpenAI."""
-    if audio_input is None:
-        return ""
-    sr, y = audio_input
-    temp_audio_path = "temp_audio.wav"
-    sf.write(temp_audio_path, y, sr)
-    with open(temp_audio_path, "rb") as audio_file:
-        transcript = client.audio.transcriptions.create(
-            model="whisper-1", 
-            file=audio_file
-        ).text
-    return transcript
-
+# Función auxiliar para extracción de características
+def extract_word_level_features(audio_path):
+    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 []
+
+# Función para la Pestaña "Práctica Conversacional"
 def chat_interaction(audio_input, history_state):
-    """
-    Gestiona una vuelta de la conversación.
-    """
     if not api_key_found: raise gr.Error("OpenAI API key not found.")
     if audio_input is None: return history_state, history_state, "", ""
-
-    # 1. Transcribir el audio del usuario
-    user_text = transcribe_audio(audio_input)
-    
-    # 2. Actualizar el historial con el mensaje del usuario
+    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
     history_state.append({"role": "user", "content": user_text})
-    
-    # Formatear para el chatbot de Gradio
-    chat_display = []
-    for i, msg in enumerate(history_state):
-        if msg['role'] == 'user':
-            chat_display.append((msg['content'], None))
-        elif msg['role'] == 'assistant':
-            if chat_display and chat_display[-1][1] is None:
-                chat_display[-1] = (chat_display[-1][0], msg['content'])
+    chat_display = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(0, len(history_state)-1, 2)]
+    chat_display.append((user_text, None))
 
-    # 3. Decidir si continuar la conversación o dar el reporte final
-    if len(history_state) < 9: # 1 system + 4 pares de user/assistant
-        # --- Continuar conversación ---
+    if len(history_state) < 9:
         messages_to_send = [{"role": "system", "content": CONVERSATION_SYSTEM_PROMPT}] + history_state
-        
-        response = client.chat.completions.create(
-            model="gpt-4o",
-            messages=messages_to_send,
-            temperature=0.7
-        )
+        response = client.chat.completions.create(model="gpt-4o", messages=messages_to_send, temperature=0.7)
         ai_response = response.choices[0].message.content
         history_state.append({"role": "assistant", "content": ai_response})
         chat_display[-1] = (chat_display[-1][0], ai_response)
-        
         return chat_display, history_state, gr.Markdown(visible=False), gr.Markdown(visible=False)
-
     else:
-        # --- Generar evaluación final ---
-        print("Generating final evaluation...")
-        messages_to_send = [{"role": "system", "content": FINAL_EVALUATION_SYSTEM_PROMPT}] + history_state
-        
-        response = client.chat.completions.create(
-            model="gpt-4o",
-            response_format={"type": "json_object"},
-            messages=messages_to_send
-        )
-        
+        print("Generating final evaluation..."); messages_to_send = [{"role": "system", "content": FINAL_EVALUATION_SYSTEM_PROMPT}] + history_state
+        response = client.chat.completions.create(model="gpt-4o", response_format={"type": "json_object"}, messages=messages_to_send)
         try:
             result = json.loads(response.choices[0].message.content)
-            
-            # Formatear el feedback en Inglés
-            fb_en = result.get('feedback_en', {})
-            md_en = f"## Final Report (CEFR Level: {result.get('cefr_level', 'N/A')})\n"
-            md_en += f"### Strengths\n{fb_en.get('strengths', '')}\n"
-            md_en += f"### Areas for Improvement\n{fb_en.get('areas_for_improvement', '')}\n"
-            md_en += "### Word-by-Word Feedback\n"
-            for item in fb_en.get('word_by_word_feedback', []):
-                md_en += f"- **{item['word']}**: {item['feedback']}\n"
-            
-            # Formatear el feedback en Español
-            fb_es = result.get('feedback_es', {})
-            md_es = f"## Reporte Final (Nivel MCERL: {result.get('cefr_level', 'N/A')})\n"
-            md_es += f"### Fortalezas\n{fb_es.get('fortalezas', '')}\n"
-            md_es += f"### Áreas a Mejorar\n{fb_es.get('areas_a_mejorar', '')}\n"
-            md_es += "### Retroalimentación por Palabra\n"
-            for item in fb_es.get('feedback_por_palabra', []):
-                md_es += f"- **{item['palabra']}**: {item['feedback']}\n"
-            
-            # Mensaje final para el chat
-            final_message = "Thank you for the conversation! Here is your final report."
-            chat_display[-1] = (chat_display[-1][0], final_message)
-            
+            fb_en = result.get('feedback_en', {}); md_en = f"## Final Report (CEFR Level: {result.get('cefr_level', 'N/A')})\n### Strengths\n{fb_en.get('strengths', '')}\n### Areas for Improvement\n{fb_en.get('areas_for_improvement', '')}\n### Word-by-Word Feedback\n"
+            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[-1] = (chat_display[-1][0], "Thank you for the conversation! Here is your final report.")
             return chat_display, history_state, 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 chat_display, history_state, gr.Markdown(value="Error generating report.", visible=True), gr.Markdown(visible=False)
+            print(f"Error parsing final report: {e}"); return chat_display, history_state, gr.Markdown(value="Error generating report.", visible=True), gr.Markdown(visible=False)
 
-# --- 3. INTERFAZ DE GRADIO ---
+# Función para la Pestaña "Evaluación por Frase"
+def run_sentence_evaluation(audio_input, reference_transcript):
+    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.", None
+    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. Please try recording again.", None
+    prompt_data = {"reference_transcript": reference_transcript, "spoken_words": word_features}
+    print("Sending detailed data to GPT-4o for 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_df = pd.DataFrame(result['word_by_word_analysis'])
+        return (result.get("overall_score_100", 0), result.get("cefr_level", "N/A"), holistic_feedback_md, gr.DataFrame(value=word_analysis_df, headers=["Reference Word", "Spoken Word", "Score", "Correct IPA", "Feedback"], interactive=False))
+    except (json.JSONDecodeError, KeyError) as e:
+        print(f"Error processing API response: {e}"); error_msg = "The API response was not in the expected format. Please try again."
+        return 0, "Error", error_msg, None
+
+# --- 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 ---
         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"
-                    )
+                    chatbot = gr.Chatbot(value=[(None, "Hi there! I'm Alex. How are you doing today?")], label="Conversation with your AI Tutor")
                     audio_in_chat = gr.Audio(sources=["microphone"], type="numpy", label="Record your response")
                 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)
-
-            # Estado para guardar el historial de la conversación
-            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]
-            )
+                    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([])
+            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"):
-            gr.Markdown("This is a placeholder for the original sentence evaluation tool.")
-            # Aquí pegarías la interfaz de la herramienta anterior si quisieras combinar ambas.
+            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):
+                    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")
+                with gr.Column(scale=2):
+                    gr.Markdown("### Assessment Summary")
+                    with gr.Row(): 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", "Correct IPA", "Feedback"], 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])
 
 if __name__ == "__main__":
-    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)
+    if not api_key_found: print("\nFATAL: OpenAI API key not found.")
+    else: demo.launch(debug=True)