Upload app.py

app.py

@@ -11,78 +11,178 @@ import whisper
 import pandas as pd
 from gtts import gTTS
 import re
-import io         # Necesario para el buffer en memoria
-import base64     # Necesario para la codificación
+import base64
+import io
 
-# --- (El resto de tu configuración y prompts se mantienen igual) ---
-# ...
+# --- 0. CONFIGURACIÓN INICIAL ---
+try:
+    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+    api_key_found = True
+except TypeError:
+    api_key_found = False
+
+print("Loading Whisper for transcription...")
+whisper_model = whisper.load_model("base", device="cpu") 
+print("Whisper model loaded.")
+
+
+# --- 1. DEFINICIÓN DE PROMPTS PARA LA IA ---
+
+CONVERSATION_SYSTEM_PROMPT = """
+You are a friendly and encouraging English language tutor named Alex. 
+A student will speak to you. Your task is to keep a natural, simple conversation going.
+1. Briefly analyze the user's previous response to estimate their CEFR level (A1, A2, B1, etc.).
+2. Formulate a simple, open-ended follow-up question that is appropriate for THAT estimated level.
+3. Your entire response must be a single, short paragraph in natural, conversational English. DO NOT use JSON.
+"""
+
+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", "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"}] }
+}
+"""
+
+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 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_en": "string", "feedback_es": "string" } ]
+}
+"""
+
+# --- 2. FUNCIONES LÓGICAS ---
+
+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 []
+
+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: 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
+    
+    # Si el historial está vacío, lo inicializamos con el prompt del sistema
+    if not history_state:
+        history_state = [{"role": "system", "content": CONVERSATION_SYSTEM_PROMPT}]
+
+    history_state.append({"role": "user", "content": user_text})
+    
+    if len(history_state) < 10: # 1 system + 4 pares de user/assistant
+        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, 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:] # Excluir el prompt de conversación
+        response = client.chat.completions.create(model="gpt-4o", response_format={"type": "json_object"}, messages=final_messages)
+        try:
+            result = json.loads(response.choices[0].message.content)
+            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 = [(history_state[i]['content'], history_state[i+1]['content']) for i in range(1, len(history_state), 2)]
+            chat_display.append((None, "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)
 
-# --- CAMBIO: La función de evaluación ahora incrusta el audio en Base64 ---
 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.", ""
-
     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']
-        
-        # --- NUEVA LÓGICA: Construir tabla en Markdown con audio Base64 ---
-        md_table = "| Reference Word | Spoken Word | Score | Feedback (EN) | Feedback (ES) | Reference Audio |\n"
-        md_table += "| :--- | :--- | :---: | :--- | :--- | :---: |\n"
-        
+        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:
-                # 1. Generar audio en un buffer en memoria (no en un archivo)
-                tts = gTTS(text=word_to_speak, lang='en')
-                mp3_fp = io.BytesIO()
-                tts.write_to_fp(mp3_fp)
-                mp3_fp.seek(0)
-                
-                # 2. Codificar el audio en Base64
+                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')
-                
-                # 3. Crear una etiqueta de audio con los datos incrustados (Data URI)
                 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 (La definición no cambia, pero ahora recibe Markdown) ---
+# --- 3. INTERFAZ DE GRADIO CON PESTAÑAS ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    # ... (Todo el resto de tu interfaz, incluyendo la Pestaña 1 y 2, se mantiene exactamente igual)
-    # ...
+    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", height=500)
+                    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)
+            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"):
-            # ... (Toda la definición de la interfaz de esta pestaña se mantiene igual)
-            # ...
-            word_analysis_out_sentence = gr.Markdown(label="Phonetic Breakdown") # <-- Esta salida ya está lista para recibir la tabla Markdown
-            # ...
-            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]
-            )
+            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.Markdown(label="Phonetic Breakdown")
+            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.")
+    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)