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README.md

@@ -1,13 +1,17 @@
 ---
-title: GLM Edge V 5B Demo
-emoji: 📉
-colorFrom: yellow
-colorTo: yellow
+title: GLM-Edge-1.5B-Chat Space
+emoji: 🐨
+colorFrom: green
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.6.0
+sdk_version: 5.7.0
 app_file: app.py
 pinned: false
-short_description: Demo for. THUDM/glm-edge-v-5b
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## GLM-Edge-1.5B-Chat Space
+
+Run with Gradio on GLM-Edge-1.5B-Chat Space.
+```shell
+python app.py
+```

app.py

@@ -0,0 +1,121 @@
+from threading import Thread
+import requests
+from io import BytesIO
+from PIL import Image
+import re
+import gradio as gr
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoImageProcessor,
+    TextIteratorStreamer,
+)
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-edge-v-5b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("THUDM/glm-edge-v-5b", trust_remote_code=True, device_map="auto").eval()
+processor = AutoImageProcessor.from_pretrained("THUDM/glm-edge-v-5b", trust_remote_code=True, device_map="auto")
+
+def get_image(image):
+    if is_url(image):
+        response = requests.get(image)
+        return Image.open(BytesIO(response.content)).convert("RGB")
+    elif image:
+        return Image.open(image).convert("RGB")
+
+def is_url(s):
+    if re.match(r'^(?:http|ftp)s?://', s):
+        return True
+    return False
+
+def preprocess_messages(history, image):
+    messages = []
+    pixel_values = None
+
+    for idx, (user_msg, model_msg) in enumerate(history):
+        if idx == len(history) - 1 and not messages:
+            messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]})
+            break
+        if user_msg:
+            messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]})
+        if model_msg:
+            messages.append({"role": "assistant", "content": [{"type": "text", "text": model_msg}]})
+    if image:
+        messages[-1]['content'].append({"type": "image"})
+        try:
+            image_input = get_image(image)
+            
+            pixel_values = torch.tensor(
+                processor(image_input).pixel_values).to(model.device)
+        except:
+            print("Invalid image path. Continuing with text conversation.")
+    return messages, pixel_values
+
+def predict(history, max_length, top_p, temperature, image=None):
+    messages, pixel_values = preprocess_messages(history, image)
+
+    model_inputs = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True
+    )
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = {
+        "input_ids": model_inputs["input_ids"].to(model.device),
+        "attention_mask": model_inputs["attention_mask"].to(model.device),
+        "streamer": streamer,
+        "max_new_tokens": max_length,
+        "do_sample": True,
+        "top_p": top_p,
+        "temperature": temperature,
+        "repetition_penalty": 1.2,
+        "eos_token_id": [59246, 59253, 59255],
+
+    }
+    if image and isinstance(pixel_values, torch.Tensor):
+        generate_kwargs['pixel_values'] = pixel_values
+    print(generate_kwargs)
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    for new_token in streamer:
+        if new_token:
+            history[-1][1] += new_token
+        yield history
+
+def main():
+    with gr.Blocks() as demo:
+        gr.HTML("""<h1 align="center">GLM-Edge-v Gradio Chat Demo</h1>""")
+
+        # Top row: Chatbot and Image upload
+        with gr.Row():
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot()
+            with gr.Column(scale=1):
+                image_input = gr.Image(label="Upload an Image", type="filepath")
+
+        # Bottom row: System prompt, user input, and controls
+        with gr.Row():
+            with gr.Column(scale=2):
+                user_input = gr.Textbox(show_label=True, placeholder="Input...", label="User Input")
+                submitBtn = gr.Button("Submit")
+                emptyBtn = gr.Button("Clear History")
+            with gr.Column(scale=1):
+                max_length = gr.Slider(0, 8192, value=4096, step=1.0, label="Maximum length", interactive=True)
+                top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
+                temperature = gr.Slider(0.01, 1, value=0.6, step=0.01, label="Temperature", interactive=True)
+
+        # Define functions for button actions
+        def user(query, history):
+            return "", history + [[query, ""]]
+        
+        # Button actions and callbacks
+        submitBtn.click(user, [user_input, chatbot], [user_input, chatbot], queue=False).then(
+            predict, [chatbot, max_length, top_p, temperature, image_input], chatbot
+        )
+        emptyBtn.click(lambda: (None, None), None, [chatbot], queue=False)
+
+    demo.queue()
+    demo.launch(server_name="127.0.0.1", server_port=7860)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,7 @@
+git+https://github.com/huggingface/transformers.git
+gradio==5.7.0
+spaces==0.30.4
+accelerate==1.1.0
+Pillow
+requests
+torch