adapt to my model

app.py

@@ -1,7 +1,68 @@
-import gradio as gr
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-def greet(name):
-    return "Hello " + name + "!!"
+peft_model_id = f"jmartin233/bloom-1b7-lora-reading-comprehension"
+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)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+
+def make_inference(product_name, product_description):
+    batch = tokenizer(
+        f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:",
+        return_tensors="pt",
+    )
+
+    with torch.cuda.amp.autocast():
+        output_tokens = model.generate(**batch, max_new_tokens=50)
+
+    
+
+def make_inference(person, location, grammer, level):
+  
+  batch = tokenizer(f"""
+    Below is a set of requirements for a short passage of English. Please write a passage that meets these requirements:
+
+    ### Requirements:
+    person: {person}
+    location: {location}. 
+    grammar: {grammar}
+    level: {level} 
+
+    ### Passage:
+    Passage:""",
+    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=2, label="Someone's name"),
+            gr.inputs.Textbox(lines=2, label="A location they might visit"),
+            gr.inputs.Textbox(lines=2, label="A type of grammar to use"),
+            gr.inputs.Textbox(lines=2, label="The leve of English to use (beginner, intermediate, advanced))"),
+
+        ],
+        gr.outputs.Textbox(label="Passage"),
+        title="Reading Comprehension",
+        description="A generative model that generates simple texts for testing reading comprehension.",
+    ).launch()