First commit

README.md

@@ -4,10 +4,12 @@ emoji: 🐨
 colorFrom: gray
 colorTo: yellow
 sdk: gradio
-sdk_version: 5.0.2
+sdk_version:  4.44.0
 app_file: app.py
 pinned: false
-short_description: Mistral Pixtral Multimodal Chatbot
+models:
+- mistralai/Pixtral-12B-2409
+short_description: Chat with Pixtral 12B Multimodal using Mistral Inference
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,59 @@
+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, AssistantMessage, 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(duration=60)
+def run_inference(message, history):
+    ## may work
+    messages = []
+    images = []
+    for couple in history:
+        if type(couple[0]) is tuple:
+            images += couple[0]
+        elif couple[0][1]:
+            messages.append(UserMessage(content = [ImageURLChunk(image_url=image_to_base64(path)) for path in images]+[TextChunk(text=couple[0][1])]))
+            messages.append(AssistantMessage(content = couple[1]))
+            images = []
+    ##
+        
+    messages.append(UserMessage(content = [ImageURLChunk(image_url=image_to_base64(file["path"])) for file in message["files"]]+[TextChunk(text=message["text"])]))
+    
+    completion_request = ChatCompletionRequest(messages=messages)
+    
+    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 result
+
+demo = gr.ChatInterface(fn=run_inference, title="Pixtral 12B", multimodal=True, description="A demo chat interface with Pixtral 12B, deployed using Mistral Inference.")
+demo.queue().launch()

requirements.txt

@@ -0,0 +1,3 @@
+mistral_inference==1.4.0
+huggingface_hub
+gradio