Commit last-minute

llama.py

@@ -0,0 +1,126 @@
+import json
+import sys
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+from huggingface_hub import login
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+LLAMA_MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+# -----------------------------
+# GLOBALS (caricati una sola volta)
+# -----------------------------
+tokenizer = None
+gen_pipe = None
+
+
+def init_model(hf_token: str):
+    """
+    Inizializza il modello LLaMA e ritorna la pipeline.
+    """
+    global tokenizer, gen_pipe
+    print("🔑 Login a HuggingFace...")
+    login(hf_token)
+
+    print("⚙️ Caricamento modello LLaMA...")
+    tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL_ID)
+    model = AutoModelForCausalLM.from_pretrained(
+        LLAMA_MODEL_ID,
+        device_map="auto",
+        torch_dtype=torch.float16
+    )
+
+    gen_pipe = pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    print("✅ Modello pronto!")
+
+
+def process_plant(plant_data, description=""):
+    """
+    Usa LLaMA per analizzare lo stato futuro della pianta.
+    Ritorna il JSON prodotto come stringa.
+    """
+    system_prompt = """You are a virtual agronomist specialized in predicting plant growth.
+
+Your knowledge includes scientific agronomy data, crop physiology, and cultivation practices already known to you from your training.
+
+You will receive structured JSON input describing the current state of a plant and its growing conditions.
+
+Your task:
+1. Base your reasoning ONLY on real agronomic knowledge you already have (do not speculate or invent).
+2. If the JSON contains the **initial size or measurable parameters** (e.g. leaf area, stem height), you may predict their future evolution with approximate numerical values.
+3. If such data are missing, DO NOT invent them — instead, give only qualitative descriptions.
+4. Always consider crop-specific knowledge (e.g. tomato vs basil).
+5. If you do not have reliable knowledge for the specific plant species in input, explicitly state this.
+6. Return ONLY a JSON object with two fields: "diagnosis" and "future_aspect".
+"""
+
+    user_prompt = f"""
+Plant data:
+{json.dumps(plant_data, ensure_ascii=False, indent=2)}
+
+Additional description:
+"{description}"
+
+Reply ONLY with the required JSON object.
+"""
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ]
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    print("🌱 Generazione descrizione con LLaMA...")
+    result = gen_pipe(
+        prompt,
+        max_new_tokens=200,
+        temperature=0.4,
+        return_full_text=False
+    )
+
+    text = result[0]["generated_text"]
+    return text
+
+
+def main():
+    # -----------------------------
+    # STEP 1: Inizializza modello
+    # -----------------------------
+    init_model("hf_*****************")  # Inserisci token HF qui
+
+    # -----------------------------
+    # STEP 2: Esegui su dati
+    # -----------------------------
+
+    output = process_plant(plant_data)
+    print("\n=== Risultato iniziale ===")
+    print(output)
+try:
+    with open("plant_summary.json", "r", encoding="utf-8") as f:
+        new_data = json.load(f)
+
+    output = process_plant(new_data)
+
+    print("\n=== Nuovo risultato ===")
+    print(output)
+
+    # 🔹 Salva il risultato in prediction.json
+    with open("prediction.json", "w", encoding="utf-8") as out_f:
+        json.dump(output, out_f, indent=2, ensure_ascii=False)
+    print("✅ Output salvato in prediction.json")
+
+except Exception as e:
+    print(f"⚠️ Errore leggendo plant_summary.json: {e}")
+
+
+# Solo se eseguito direttamente
+if __name__ == "__main__":
+    main()