Update app.py

app.py

@@ -26,9 +26,6 @@ IMAGE_CACHE_DIRECTORY = "/tmp"
 IMAGE_WIDTH = 512
 CHAT_HISTORY = List[Tuple[Optional[Union[Tuple[str], str]], Optional[str]]]
 
-# Configurar la API de Gemini
-genai.configure(api_key=GOOGLE_API_KEY)
-
 # Función para transformar el historial del chat
 def transform_history(history: CHAT_HISTORY):
     """
@@ -43,27 +40,23 @@ def transform_history(history: CHAT_HISTORY):
     return transformed
 
 # Función de generación de respuesta
-def response(
-    message: str, history: CHAT_HISTORY, model_choice: str, system_instruction: str
-) -> str:
+def response(message: str, history: CHAT_HISTORY, model: genai.GenerativeModel):
     """
     Genera una respuesta basada en el historial del chat y el mensaje del usuario.
     """
-    generation_config = genai.types.GenerationConfig(
-        temperature=0.7,
-        max_output_tokens=8192,
-        top_k=10,
-        top_p=0.9
+    # Crear el input para el modelo basado en el historial y el mensaje del usuario
+    input_text = "\n".join(
+        [
+            f"User: {item[0]}" if item[0] else f"Bot: {item[1]}"
+            for item in history
+        ]
     )
-
-    model = genai.GenerativeModel(
-        model_name=model_choice,
-        generation_config=generation_config,
-        system_instruction=system_instruction,
-    )
-
-    transformed_history = transform_history(history)
-    model_response = model.chat(messages=transformed_history + [{"role": "user", "content": message}])
+    input_text += f"\nUser: {message}"
+    
+    # Generar la respuesta
+    model_response = model.generate(input_text)
+    
+    # Retornar la respuesta generada
     return model_response.text
 
 # Preprocesamiento de imágenes
@@ -83,7 +76,7 @@ def cache_pil_image(image: Image.Image) -> str:
 # Subir imágenes
 def upload(files: Optional[List[str]], chatbot: CHAT_HISTORY) -> CHAT_HISTORY:
     for file in files:
-        image = Image.open(file).convert("RGB")
+        image = Image.open(file).convert('RGB')
         image_preview = preprocess_image(image)
         if image_preview:
             gr.Image(image_preview).render()
@@ -102,10 +95,32 @@ def bot(
     files: Optional[List[str]],
     model_choice: str,
     system_instruction: str,
-    chatbot: CHAT_HISTORY,
+    chatbot: CHAT_HISTORY
 ):
+    if not GOOGLE_API_KEY:
+        raise ValueError("GOOGLE_API_KEY is not set.")
+
+    # Configurar la API con la clave
+    genai.configure(api_key=GOOGLE_API_KEY)
+    generation_config = genai.types.GenerationConfig(
+        temperature=0.7,
+        max_output_tokens=8192,
+        top_k=10,
+        top_p=0.9
+    )
+
     text_prompt = chatbot[-1][0] if chatbot and chatbot[-1][0] and isinstance(chatbot[-1][0], str) else ""
-    bot_reply = response(text_prompt, chatbot, model_choice, system_instruction)
+    transformed_history = transform_history(chatbot)
+
+    # Crear el modelo con la instrucción del sistema
+    model = genai.GenerativeModel(
+        model_name=model_choice,
+        generation_config=generation_config,
+        system_instruction=system_instruction
+    )
+
+    # Generar la respuesta usando la función `response`
+    bot_reply = response(text_prompt, transformed_history, model)
     chatbot[-1] = (text_prompt, bot_reply)
     return chatbot
 
@@ -114,7 +129,7 @@ system_instruction_component = gr.Textbox(
     placeholder="Enter system instruction...", show_label=True, scale=8
 )
 chatbot_component = gr.Chatbot(
-    label="Gemini",
+    label='Gemini',
     bubble_full_width=False,
     scale=2,
     height=300
@@ -135,14 +150,14 @@ model_choice_component = gr.Dropdown(
 
 user_inputs = [
     text_prompt_component,
-    chatbot_component,
+    chatbot_component
 ]
 
 bot_inputs = [
     upload_button_component,
     model_choice_component,
     system_instruction_component,
-    chatbot_component,
+    chatbot_component
 ]
 
 # Interfaz de usuario