Delete app.py

app.py

@@ -1,39 +0,0 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-import torch
-
-# Load your model and tokenizer using the adapter weights
-model_name = "mherrador/CE-5.0"  # Replace with your actual model name
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16,
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    quantization_config=bnb_config,
-    device_map="auto",
-    trust_remote_code=True,
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Function to generate recommendations
-def generate_recommendations(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
-    outputs = model.generate(**inputs, max_new_tokens=128)
-    recommendations = tokenizer.batch_decode(outputs)[0]
-    return recommendations
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=generate_recommendations,
-    inputs=gr.Textbox(lines=5, placeholder="Enter your questions here..."),
-    outputs=gr.Textbox(lines=10),
-    title="Circular Economy Recommender",
-    description="Enter your questions about circular economy practices to get recommendations.",
-)
-
-# Launch the interface
-iface.launch()