Update app.py

app.py

@@ -1,64 +1,49 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the DeepSeek-R1-Distill-Qwen-1.5B-uncensored model
+model_id = "thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,  # Use float16 for efficiency
+    low_cpu_mem_usage=True,
+    device_map="auto"  # Automatically use available devices
+)
 
-"""
-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)",
-        ),
+def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
+    """Generate text based on prompt"""
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Generate
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=inputs.input_ids,
+            attention_mask=inputs.attention_mask,
+            max_length=len(inputs.input_ids[0]) + max_length,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+        )
+    
+    # Decode and return only the generated part
+    generated_text = tokenizer.decode(generation_output[0], skip_special_tokens=True)
+    return generated_text
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Enter your prompt here...", label="Prompt"),
+        gr.Slider(minimum=10, maximum=500, value=100, step=10, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p")
     ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="DeepSeek-R1-Distill-Qwen-1.5B Demo",
+    description="Enter a prompt to generate text with the DeepSeek-R1-Distill-Qwen-1.5B-uncensored model."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app
+demo.launch()