Update app.py

app.py

@@ -41,7 +41,8 @@ except Exception as e:
     print(f"ERRO ao inicializar o cliente Groq: {e}.")
 
 
-# --- ROTA 1: TEXT-TO-SPEECH (TTS) ---
+# --- ROTAS PRINCIPAIS ---
+
 @app.route('/tts-proxy', methods=['POST'])
 def tts_proxy():
     data = request.get_json()
@@ -53,57 +54,85 @@ def tts_proxy():
         mp3_fp = io.BytesIO()
         tts.write_to_fp(mp3_fp)
         mp3_fp.seek(0)
-        return send_file(mp3_fp, mimetype='audio/mpeg', as_attachment=True, download_name='audio.mp3')
+        return send_file(mp3_fp, mimetype='audio/mpeg')
     except Exception as e:
         return jsonify({"error": f"Failed to generate audio via gTTS: {e}"}), 500
 
-
-# --- ROTA 2: ANÁLISE DE TEXTO (FLASHCARDS, ETC.) ---
 @app.route('/explain-proxy', methods=['POST'])
 def explain_proxy():
     data = request.get_json()
-    # CORREÇÃO: Atualizado o modelo padrão para a versão mais recente
     model_provider, model_name = data.get('model', 'gemini:gemini-2.5-flash-latest').split(':', 1)
-    word, context = data.get('word', '').strip(), data.get('context', '')
-    for_flashcard, context_focus = data.get('for_flashcard', False), data.get('context_focus', 'General/Social')
+    context_focus = data.get('context_focus', 'General/Social')
     custom_prompt = data.get('custom_prompt', None)
+    word = data.get('word', '').strip()
+    context = data.get('context', '')
+    for_flashcard = data.get('for_flashcard', False)
 
     if (model_provider == 'gemini' and not genai_client) or (model_provider == 'groq' and not groq_client):
         return jsonify({"error": f"{model_provider.upper()}_API_KEY not configured."}), 503
     
-    system_instruction_base = f"You are a professional English tutor focused on the context '{context_focus}'."
+    system_instruction_base = f"You are a professional English tutor. The user's study focus is '{context_focus}'. All your responses must be in ENGLISH."
     try:
-        # Se for uma requisição de Geração de Atividade (custom_prompt)
         if custom_prompt:
-             # A resposta será texto simples, então retornamos em um campo JSON
             activity_text = get_ai_text_response(model_provider, model_name, system_instruction_base, custom_prompt)
-            return jsonify({"explanation": activity_text}) # Reutilizando a chave 'explanation'
+            return jsonify({"explanation": activity_text})
         
         if not word: return jsonify({"error": "No word selected."}), 400
         
-        # Lógica para Flashcard
         if for_flashcard:
             schema = {"type": "object", "properties": {"term": {"type": "string"}, "translation": {"type": "string"}, "context_sentence": {"type": "string"}, "gapped_sentence": {"type": "string"}, "definition": {"type": "string"}}, "required": ["term", "translation", "context_sentence", "gapped_sentence", "definition"]}
-            prompt = f"Analyze '{word}' in context: '{context}'. Generate a JSON for a flashcard. The 'gapped_sentence' must replace '{word}' with '______________'."
+            prompt = f"Analyze '{word}' in context: '{context}'. Generate a JSON for a flashcard. The 'gapped_sentence' must replace '{word}' with '______________'. You must strictly follow the JSON schema and provide valid, non-empty values for all fields."
             return jsonify(get_ai_text_response(model_provider, model_name, system_instruction_base, prompt, json_schema=schema))
         
-        # Lógica para tradução rápida
-        else:
+        else: # Quick translation logic (not currently used in UI, but kept for potential future use)
             prompt = f"Analyze '{word}' in context: '{context}'. Provide a one-sentence English explanation, then '---', then the Portuguese translation."
             parts = get_ai_text_response(model_provider, model_name, system_instruction_base, prompt).split('---', 1)
             return jsonify({"explanation": parts[0].strip(), "translation": parts[1].strip() if len(parts) > 1 else 'N/A'})
+
     except Exception as e:
         return jsonify({"error": f"AI analysis failed: {e}"}), 500
 
-# --- ROTA 3: ANÁLISE DE IMAGEM ---
+# --- NOVA ROTA PARA FEEDBACK DE ATIVIDADES ---
+@app.route('/activity-feedback', methods=['POST'])
+def activity_feedback():
+    data = request.get_json()
+    model_provider, model_name = data.get('model', 'gemini:gemini-2.5-flash-latest').split(':', 1)
+    context_focus = data.get('context_focus', 'General/Social')
+    original_prompt = data.get('original_prompt', '')
+    user_response = data.get('user_response', '')
+
+    if not original_prompt or not user_response:
+        return jsonify({"error": "Original prompt and user response are required."}), 400
+
+    if (model_provider == 'gemini' and not genai_client) or (model_provider == 'groq' and not groq_client):
+        return jsonify({"error": f"{model_provider.upper()}_API_KEY not configured."}), 503
+
+    system_instruction = (
+        "You are an expert English teacher providing feedback. "
+        f"The user's study focus is '{context_focus}'. "
+        "Your entire response MUST be in English. "
+        "Provide clear, constructive feedback on the user's writing. "
+        "Point out grammar, spelling, or style errors. "
+        "Offer a corrected or improved version of their text. "
+        "Structure your feedback with markdown for clarity (e.g., using ### Corrected Version)."
+    )
+    
+    user_prompt = f"The original task was: \"{original_prompt}\"\n\nHere is the user's response:\n---\n{user_response}\n---\nPlease provide your feedback."
+
+    try:
+        feedback_text = get_ai_text_response(model_provider, model_name, system_instruction, user_prompt)
+        return jsonify({"feedback": feedback_text})
+    except Exception as e:
+        return jsonify({"error": f"AI feedback failed: {e}"}), 500
+
+
 @app.route('/analyze-image', methods=['POST'])
 def analyze_image():
     if not genai_client: return jsonify({"error": "GEMINI_API_KEY not configured."}), 503
     data = request.get_json()
     base64_image = data.get('image')
-    # CORREÇÃO: Pega o modelo do seletor, com um padrão seguro caso não seja um modelo Gemini.
     model_value = data.get('model', 'gemini:gemini-2.5-flash-latest')
-    # Garante que estamos usando um modelo Gemini para análise de imagem
+    
     model_name = 'gemini-2.5-flash-latest'
     if model_value.startswith('gemini:'):
         model_name = model_value.split(':', 1)[1]
@@ -111,16 +140,14 @@ def analyze_image():
     if not base64_image: return jsonify({"error": "No image data."}), 400
     try:
         image = Image.open(io.BytesIO(base64.b64decode(base64_image.split(',')[1])))
-        # CORREÇÃO: Usa o nome do modelo dinâmico
         model = genai_client.GenerativeModel(model_name)
         schema = { "type": "object", "properties": { "vocabulary": { "type": "array", "items": { "type": "object", "properties": { "term": {"type": "string"}, "definition": {"type": "string"} }, "required": ["term", "definition"] } } }, "required": ["vocabulary"] }
-        prompt = [ "Identify 5-7 key objects/concepts in this image. For each, provide its English name and a simple definition. Return a single JSON object conforming to the schema.", image ]
+        prompt = [ "Act as an English teacher. Identify 5-7 key objects/concepts in this image. For each, provide its English name and a simple definition. Return a single JSON object conforming to the schema.", image ]
         response = model.generate_content(prompt, generation_config={"response_mime_type": "application/json", "response_schema": schema})
         return jsonify(json.loads(response.text)['vocabulary'])
     except Exception as e:
         return jsonify({"error": f"Image analysis failed: {e}"}), 500
 
-# --- ROTA 4: CHAT COM IA ---
 @app.route('/chat-with-ai', methods=['POST'])
 def chat_with_ai():
     if not groq_client: return jsonify({"error": "GROQ_API_KEY not configured."}), 503
@@ -130,38 +157,33 @@ def chat_with_ai():
     try:
         system = "You are 'Groq Chat', a friendly English tutor. Keep responses concise (1-2 sentences). If the user makes a grammar mistake, gently correct it. Ask questions to keep the conversation flowing. Always respond in English."
         messages = [{"role": "system", "content": system}] + history + [{"role": "user", "content": user_message}]
-        # Mantido fixo para garantir a experiência de baixa latência
         response = groq_client.chat.completions.create(model="llama-3.1-8b-instant", messages=messages, temperature=0.7)
         return jsonify({"response": response.choices[0].message.content.strip()})
     except Exception as e:
         return jsonify({"error": f"AI chat failed: {e}"}), 500
 
-# --- ROTA 5: ANÁLISE DE PRONÚNCIA ---
 @app.route('/pronunciation-feedback', methods=['POST'])
 def pronunciation_feedback():
-    if not groq_client: return jsonify({"error": "GROQ_API_KEY not configured for pronunciation analysis."}), 503
+    if not groq_client: return jsonify({"error": "GROQ_API_KEY not configured."}), 503
     data = request.get_json()
     target_text, user_text = data.get('target_text'), data.get('user_text')
-    if not target_text or not user_text: return jsonify({"error": "Target and user text are required."}), 400
+    if not target_text or not user_text: return jsonify({"error": "Required data missing."}), 400
     try:
         system_instruction = "You are an expert American English pronunciation coach. The user tried to say a target sentence, and their speech was transcribed. Based on the likely pronunciation differences, provide brief, friendly, and actionable feedback in Portuguese. Focus on 1-2 key points. If it's very close, praise the user."
         user_prompt = f"Target: \"{target_text}\"\nTranscription: \"{user_text}\"\n\nProvide pronunciation feedback."
         messages = [{"role": "system", "content": system_instruction}, {"role": "user", "content": user_prompt}]
-         # Mantido fixo para garantir a experiência de baixa latência
         response = groq_client.chat.completions.create(model="llama-3.1-8b-instant", messages=messages, temperature=0.5)
         return jsonify({"feedback": response.choices[0].message.content.strip()})
     except Exception as e:
         return jsonify({"error": f"Pronunciation analysis failed: {e}"}), 500
 
-# --- ROTA 6: GERAÇÃO DE IMAGEM ---
 @app.route('/generate-image', methods=['POST'])
 def generate_image():
-    if not genai_client: return jsonify({"error": "GEMINI_API_KEY not configured for image generation."}), 503
+    if not genai_client: return jsonify({"error": "GEMINI_API_KEY not configured."}), 503
     data = request.get_json()
     prompt = data.get('prompt')
     if not prompt: return jsonify({"error": "Image prompt is required."}), 400
     try:
-        # CORREÇÃO: Atualizado para o modelo de geração de imagem mais recente
         model = genai_client.GenerativeModel(model_name='gemini-2.5-flash-image-preview')
         response = model.generate_content(prompt)
         base64_image_data = response.parts[0].inline_data.data