Update app.py

app.py

@@ -75,6 +75,8 @@ def translate_text(text, target_lang):
 
 # ---------------- LLM VIA HF API ----------------
 def generate_prevention_llm(plant, disease):
+    HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
+
     if not HF_API_TOKEN:
         return "⚠️ Hugging Face API token not found."
 
@@ -125,22 +127,37 @@ Irrigation, drainage, sanitation, hygiene, storage.
         "parameters": {
             "max_new_tokens": 300,
             "temperature": 0.7,
-            "top_p": 0.9
+            "top_p": 0.9,
+            "return_full_text": False
         }
     }
 
+    headers = {
+        "Authorization": f"Bearer {HF_API_TOKEN}",
+        "Content-Type": "application/json"
+    }
+
     try:
-        response = requests.post(HF_API_URL, headers=HEADERS, json=payload, timeout=60)
-        response.raise_for_status()
+        response = requests.post(
+            "https://api-inference.huggingface.co/models/cropinailab/aksara_v1",
+            headers=headers,
+            json=payload,
+            timeout=(10, 60)
+        )
+
         output = response.json()
 
-        if isinstance(output, list):
-            return output[0]["generated_text"].replace(prompt, "").strip()
+        # Robust HF response handling
+        if isinstance(output, list) and len(output) > 0:
+            return output[0].get("generated_text", "").strip()
+
+        if isinstance(output, dict) and "error" in output:
+            return f"LLM Error: {output['error']}"
 
-        return "No response from LLM."
+        return "⚠️ No response from LLM."
 
     except Exception as e:
-        return f"LLM Error: {e}"
+        return f"LLM Error: {str(e)}"
 
 # ---------------- PREDICTION ----------------
 def predict(image_input, plant, language):