Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from threading import Thread
+
+# Loading the tokenizer and model from Hugging Face's model hub.
+tokenizer = AutoTokenizer.from_pretrained("cnmoro/jack-68m-text-structurization")
+model = AutoModelForCausalLM.from_pretrained("cnmoro/jack-68m-text-structurization")
+
+# using CUDA for an optimal experience
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+
+# Function to generate model predictions.
+def predict(message, history):
+    
+    model_inputs = tokenizer([
+        f"### Structurize: {message}\n\n### Response:\n"
+    ], return_tensors="pt").to(device)
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=512,
+        top_p=0.2,
+        top_k=20,
+        temperature=0.1,
+        num_beams=1
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()  # Starting the generation in a separate thread.
+    partial_message = ""
+    for new_token in streamer:
+        partial_message += new_token
+        if '</s>' in partial_message:  # Breaking the loop if the stop token is generated.
+            break
+        yield partial_message
+
+# Setting up the Gradio chat interface.
+gr.ChatInterface(predict,
+                 title="TextStructurization_Jack68m_CPU",
+                 description="Pass a text to be structurized"
+                 ).launch()  # Launching the web interface.