New app.py

app.py

@@ -1,64 +1,194 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import base64
+import io
+from PIL import Image
+import requests
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+# Inicialización del cliente de inferencia con el modelo especificado
 client = InferenceClient("mistralai/Pixtral-Large-Instruct-2411")
 
+def image_to_base64(image_path):
+    """Convert an image file to a base64 string."""
+    with open(image_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
+    return encoded_string
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def base64_to_image(base64_string):
+    """Convert a base64 string to an image."""
+    image_data = base64.b64decode(base64_string)
+    image = Image.open(io.BytesIO(image_data))
+    return image
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def describe_image(image, system_message, max_tokens, temperature, top_p):
+    """Describe an image using the model."""
+    if image is None:
+        return "No image uploaded.", []
 
-    messages.append({"role": "user", "content": message})
+    # Convert image to base64
+    buffered = io.BytesIO()
+    image.save(buffered, format="JPEG")
+    image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
 
-    response = ""
+    messages = [
+        {"role": "system", "content": system_message},
+        {"role": "user", "content": "Describe the following image:"},
+        {"role": "user", "content": image_base64}
+    ]
 
-    for message in client.chat_completion(
+    response = ""
+    for chunk in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
-
+        token = chunk.choices[0].delta.content
         response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
+    return response, [(f"User: Describe the following image:", response)]
+
+def respond(
+    user_message: str,
+    chat_history: list[tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+) -> str:
+    """
+    Función para generar respuestas basadas en el historial de chat y parámetros de configuración.
+
+    Args:
+        user_message (str): Mensaje del usuario.
+        chat_history (list[tuple[str, str]]): Historial de chat.
+        system_message (str): Mensaje del sistema que define el comportamiento del chatbot.
+        max_tokens (int): Máximo número de tokens a generar.
+        temperature (float): Temperatura para el muestreo de texto.
+        top_p (float): Parámetro top-p para el muestreo de texto.
+
+    Yields:
+        str: Respuesta generada por el modelo.
+    """
+    # Construcción de la lista de mensajes
+    messages = [{"role": "system", "content": system_message}]
+    for user_msg, assistant_msg in chat_history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
+    messages.append({"role": "user", "content": user_message})
+
+    response = ""
+
+    try:
+        # Obtención de la respuesta del modelo
+        for chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = chunk.choices[0].delta.content
+            response += token
+            yield response
+    except Exception as e:
+        yield f"Error al obtener respuesta: {str(e)}"
+
+def main():
+    """
+    Función principal para iniciar la interfaz de chat.
+    """
+    def update_chat(user_message, image, chat_history, system_message, max_tokens, temperature, top_p):
+        if image is not None:
+            description, new_history = describe_image(image, system_message, max_tokens, temperature, top_p)
+            chat_history.extend(new_history)
+            user_message = description
+        if user_message:
+            response_generator = respond(
+                user_message,
+                chat_history,
+                system_message,
+                max_tokens,
+                temperature,
+                top_p,
+            )
+            for response in response_generator:
+                chat_history.append((user_message, response))
+                yield "", chat_history, chat_history
+        else:
+            yield "", chat_history, chat_history
+
+    with gr.Blocks(title="Chatbot con MistralAI", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# Chatbot con MistralAI")
+        gr.Markdown("Un chatbot amigable basado en el modelo MistralAI Pixtral-Large-Instruct-2411 que puede describir imágenes y mantener un historial de chat.")
+
+        with gr.Row():
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot(label="Conversación")
+                user_message = gr.Textbox(label="Mensaje del Usuario", placeholder="Escribe tu mensaje aquí...")
+                with gr.Row():
+                    submit_button = gr.Button("Enviar")
+                    clear_button = gr.Button("Limpiar")
+
+            with gr.Column(scale=2):
+                image_input = gr.Image(label="Cargar Imagen", type="pil")
+                image_description = gr.Textbox(label="Descripción de la Imagen", interactive=False)
+
+        with gr.Row():
+            system_message = gr.Textbox(
+                value="You are a friendly Chatbot.",
+                label="Mensaje del Sistema",
+                placeholder="Define el comportamiento del chatbot."
+            )
+            max_tokens = gr.Slider(
+                minimum=1,
+                maximum=2048,
+                value=512,
+                step=1,
+                label="Max New Tokens",
+                info="Máximo número de tokens generados."
+            )
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=4.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature",
+                info="Controla la creatividad de la respuesta."
+            )
+            top_p = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.95,
+                step=0.05,
+                label="Top-p (Nucleus Sampling)",
+                info="Parámetro para el muestreo del texto."
+            )
+
+        chat_history = gr.State([])
+
+        submit_button.click(
+            fn=update_chat,
+            inputs=[user_message, image_input, chat_history, system_message, max_tokens, temperature, top_p],
+            outputs=[user_message, chatbot, chat_history]
+        )
+
+        clear_button.click(
+            fn=lambda: ([], [], []),
+            inputs=[],
+            outputs=[user_message, chatbot, chat_history]
+        )
+
+        image_input.upload(
+            fn=describe_image,
+            inputs=[image_input, system_message, max_tokens, temperature, top_p],
+            outputs=[image_description, chat_history]
+        )
 
-if __name__ == "__main__":
     demo.launch()
+
+if __name__ == "__main__":
+    main()