Update app.py

app.py

@@ -1,20 +1,14 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-# Load text-to-text generation pipeline
-generator = pipeline("text2text-generation", model="Qwen/Qwen2.5-7B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B", device_map="auto", trust_remote_code=True)
 
-def generate_text(input_text):
-    result = generator(input_text, max_length=100, clean_up_tokenization_spaces=True)
-    return result[0]['generated_text']
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Gradio UI
-interface = gr.Interface(
-    fn=generate_text,
-    inputs=gr.Textbox(lines=4, placeholder="Enter your text prompt here..."),
-    outputs="text",
-    title="Text-to-Text Generator AI",
-    description="This AI generates text based on your input using a Hugging Face model."
-)
+gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Qwen Text Generator").launch()
 
-interface.launch()