Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import prepare_model_for_kbit_training
+from peft import LoraConfig, get_peft_model
+from datasets import load_dataset
+import transformers
+
+# load model from hub
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+
+model_name = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
+model = AutoModelForCausalLM.from_pretrained(model_name,
+                                             device_map="auto",
+                                             trust_remote_code=False,
+                                             revision="main")
+
+config = PeftConfig.from_pretrained("saanvi-bot/jayson")
+model = PeftModel.from_pretrained(model, "saanvi-bot/jayson")
+
+# load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+"""### Use Fine-tuned Model"""
+
+intstructions_string = f"""convert into json format \n"""
+
+prompt_template = lambda comment: f'''[INST] {intstructions_string} \n{comment} \n[/INST]'''
+
+model.eval()
+
+# Streamlit interface
+st.title("Text to JSON Converter")
+st.write("Enter the text you want to convert to JSON format:")
+
+# Text input from the user
+user_input = st.text_area("Input text", height=200)
+
+# Convert input text to JSON
+if st.button("Convert"):
+    prompt = prompt_template(user_input)
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=200)
+
+    json_output = tokenizer.batch_decode(outputs)[0]
+    st.json(json_output)