add app and requirements

app.py

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+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = f"telmo000/bloom-positive-reframing"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict=True,
+    load_in_8bit=True,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+
+def make_inference(original_text):
+    batch = tokenizer(
+        f"### Negative sentence:\n{original_text}\n\n### Reframing strategy: ['optimism']\n\n### Reframing sentence:\n",
+        return_tensors="pt",
+    )
+
+    with torch.cuda.amp.autocast():
+        output_tokens = model.generate(**batch, max_new_tokens=50)
+
+    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+
+if __name__ == "__main__":
+    # make a gradio interface
+    import gradio as gr
+
+    gr.Interface(
+        make_inference,
+        [
+            gr.inputs.Textbox(lines=3, label="Original Text"),
+        ],
+        gr.outputs.Textbox(label="Ad"),
+        title="Bloom positive reframing",
+        description="Bloom positive reframing is a BLOOM-base generative model adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting the original meaning. ",
+    ).launch()

requirements.txt

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+bitsandbytes 
+datasets 
+accelerate 
+loralib
+gradio
+git+https://github.com/huggingface/peft.git
+git+https://github.com/huggingface/transformers.git@main