Upload app.py

app.py

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+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Load base model and LoRA adapter
+base_model_id = "openlm-research/open_llama_3b"
+adapter_path = "jalonso24/lora-lateblight-v3"
+
+base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float32)
+model = PeftModel.from_pretrained(base_model, adapter_path)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+model.eval()
+
+# Inference function
+def predict_risk(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=20,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio UI
+examples = [
+    [
+        "La variedad es INIA-302 Amarilis, sembrada en noviembre.\nLa precipitación fue 18.4 mm, la temperatura máxima 17.2°C, la mínima 6.1°C y la humedad promedio 84.12%.\n\n¿Cuál es el riesgo de tizón tardío?"
+    ]
+]
+
+description = """
+### 🧪 Predict Late Blight Risk using `lora-lateblight-v3`
+Enter a weather and crop scenario in Spanish, and the model will respond with the predicted level of late blight risk.
+"""
+
+gr.Interface(
+    fn=predict_risk,
+    inputs=gr.Textbox(lines=6, label="Input Prompt (in Spanish)"),
+    outputs=gr.Textbox(lines=4, label="Model Prediction"),
+    title="🌱 Late Blight Risk Predictor",
+    description=description,
+    examples=examples
+).launch()