Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+# Load your model and tokenizer using the adapter weights
+model_name = "mherrador/CE5.0_expert_v2"  # 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()