Create app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+from gradio.data_classes import FileData
+from huggingface_hub import snapshot_download
+from pathlib import Path
+import base64
+import spaces
+import os
+
+from mistral_inference.transformer import Transformer
+from mistral_inference.generate import generate
+
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageURLChunk
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+models_path = Path.home().joinpath('pixtral', 'Pixtral')
+models_path.mkdir(parents=True, exist_ok=True)
+
+snapshot_download(repo_id="mistral-community/pixtral-12b-240910", 
+                  allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], 
+                  local_dir=models_path)
+
+tokenizer = MistralTokenizer.from_file(f"{models_path}/tekken.json")
+model = Transformer.from_folder(models_path)
+
+def image_to_base64(image_path):
+    with open(image_path, 'rb') as img:
+        encoded_string = base64.b64encode(img.read()).decode('utf-8')
+    return f"data:image/jpeg;base64,{encoded_string}"
+
+@spaces.GPU
+def run_inference(image_url, prompt):
+    base64 = image_to_base64(image_url)
+    completion_request = ChatCompletionRequest(messages=[UserMessage(content=[ImageURLChunk(image_url=base64), TextChunk(text=prompt)])])
+    
+    encoded = tokenizer.encode_chat_completion(completion_request)
+    
+    images = encoded.images
+    tokens = encoded.tokens
+    
+    out_tokens, _ = generate([tokens], model, images=[images], max_tokens=512, temperature=0.45, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+    result = tokenizer.decode(out_tokens[0])
+    return [[prompt, result]]
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        image_box = gr.Image(type="filepath")
+   
+        chatbot = gr.Chatbot(
+            scale = 2,
+            height=750
+        )
+    text_box = gr.Textbox(
+            placeholder="Enter text and press enter, or upload an image",
+            container=False,
+        )
+
+
+    btn = gr.Button("Submit")
+    clicked = btn.click(run_inference,
+                        [image_box,text_box],
+                        chatbot
+                        )
+
+demo.queue().launch()