Update app.py

app.py

@@ -62,7 +62,8 @@ def tts_proxy():
 @app.route('/explain-proxy', methods=['POST'])
 def explain_proxy():
     data = request.get_json()
-    model_provider, model_name = data.get('model', 'gemini:gemini-1.5-flash-latest').split(':', 1)
+    # 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')
     custom_prompt = data.get('custom_prompt', None)
@@ -100,10 +101,18 @@ 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]
+
     if not base64_image: return jsonify({"error": "No image data."}), 400
     try:
         image = Image.open(io.BytesIO(base64.b64decode(base64_image.split(',')[1])))
-        model = genai_client.GenerativeModel('gemini-1.5-flash-latest')
+        # 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 ]
         response = model.generate_content(prompt, generation_config={"response_mime_type": "application/json", "response_schema": schema})
@@ -121,6 +130,7 @@ 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:
@@ -137,6 +147,7 @@ def pronunciation_feedback():
         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:
@@ -150,7 +161,8 @@ def generate_image():
     prompt = data.get('prompt')
     if not prompt: return jsonify({"error": "Image prompt is required."}), 400
     try:
-        model = genai_client.GenerativeModel(model_name='gemini-1.5-flash-image-preview')
+        # 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
         return jsonify({"image_base64": base64_image_data})