Create test01.py

test01.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load the model and tokenizer
+model_name = "Reverb/Mistral-7B-LoreWeaver"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Initialize the pipeline
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+def generate_story(prompt):
+    # Generate a response using the model
+    responses = generator(prompt, max_length=200, num_return_sequences=1)
+    return responses[0]['generated_text']
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_story,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here..."),
+    outputs=gr.Textbox(label="Generated Story"),
+    title="Mistral-7B-LoreWeaver Story Generator",
+    description="Enter a prompt to generate a narrative text using the Mistral-7B-LoreWeaver model."
+)
+
+# Launch the interface
+iface.launch()