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README.md

@@ -1,12 +1,12 @@
 ---
-title: FluxiIA LLama-Florence
-emoji: 🌖
-colorFrom: pink
-colorTo: pink
+title: FluxiAI ChatbotVision
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
 sdk: gradio
-sdk_version: 5.5.0
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

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+import spaces
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+from huggingface_hub import InferenceClient
+import io
+from PIL import Image
+import torch
+import numpy as np
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = 'J-LAB/Florence-vl3'
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to(device).eval()
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+@spaces.GPU
+def run_example(task_prompt, image):
+    inputs = processor(text=task_prompt, images=image, return_tensors="pt", padding=True).to(device)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        early_stopping=False,
+        do_sample=False,
+        num_beams=3,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(
+        generated_text,
+        task=task_prompt,
+        image_size=(image.width, image.height)
+    )
+    return parsed_answer
+
+def process_image(image, task_prompt):
+    if isinstance(image, str):  # Check if the image path is provided
+        image = Image.open(image)
+    elif isinstance(image, np.ndarray):
+        image = Image.fromarray(image)  # Convert NumPy array to PIL Image
+    
+    if task_prompt == 'Product Caption':
+        task_prompt = '<MORE_DETAILED_CAPTION>'
+    elif task_prompt == 'OCR':
+        task_prompt = '<OCR>'
+    
+    results = run_example(task_prompt, image)
+    
+    # Remove the key and get the text value
+    if results and task_prompt in results:
+        output_text = results[task_prompt]
+    else:
+        output_text = ""
+
+    return output_text
+
+# Inicializando o cliente
+client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+
+# Função de resposta para o chatbot
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, image):
+    image_result = ""
+    if image is not None:
+        try:
+            image_result_caption = process_image(image, 'Product Caption')
+            image_result_ocr = process_image(image, 'OCR')
+            image_result = image_result_caption + " " + image_result_ocr  # Concatenar os resultados
+        except Exception as e:
+            image_result = f"An error occurred with image processing: {str(e)}"
+
+    # Construindo a mensagem completa com o resultado da imagem
+    full_message = message
+    if image_result:
+        full_message = f"\n<image>{image_result}</image>\n\n{message}"
+
+    # Adicionando mensagens ao histórico
+    messages = [{"role": "system", "content": f'{system_message} a descrição das imagens enviadas pelo usuário ficam dentro da tag <image> </image>'}]
+    for user, assistant in history:
+        if user:
+            messages.append({"role": "user", "content": user})
+        if assistant:
+            messages.append({"role": "assistant", "content": assistant})
+
+    messages.append({"role": "user", "content": full_message})
+
+    # Gerando a resposta
+    response = ""
+    try:
+        for msg in client.chat_completion(messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p):
+            token = msg.choices[0].delta.content
+            response += token
+    except Exception as e:
+        response = f"An error occurred: {str(e)}"  # Retornando apenas o texto da mensagem de erro
+    
+    # Atualizando o histórico, mas sem mostrar image_result no chat
+    history.append((message, response))
+    return history, gr.update(value=None), gr.update(value="")
+
+# Configurando a interface do Gradio
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    chat_input = gr.Textbox(placeholder="Enter message...", show_label=False)
+    image_input = gr.Image(type="filepath", label="Upload an image")
+    submit_btn = gr.Button("Send Message")
+    system_message = gr.Textbox(value="Você é um chatbot útil que sempre responde em português", label="System message")
+    max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+    temperature = gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature")
+    top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+    submit_btn.click(respond, inputs=[chat_input, chatbot, system_message, max_tokens, temperature, top_p, image_input], outputs=[chatbot, image_input, chat_input])
+
+if __name__ == "__main__":
+    demo.launch(debug=True, quiet=True)

pre-requirements.txt

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+pip>=23.0.0

requirements.txt

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+huggingface_hub
+spaces
+transformers
+timm
+openai