Update app.py

app.py

@@ -38,7 +38,7 @@ def search_digikey_components(keywords: str) -> dict:
     }
     payload = {
         "Keywords": keywords,
-        "Limit": 50,
+        "Limit": 10,
         "Offset": 0,
         "FilterOptionsRequest": {
             "MarketPlaceFilter": "NoFilter",
@@ -65,10 +65,16 @@ def load_t5_model():
 # Generate advice for a component
 def generate_advice(product: dict, tokenizer, model) -> str:
     description = product.get("Description", {}).get("DetailedDescription", "No description available.")
-    prompt = f"Provide advice for using this electronic component: {description}"
+    category = product.get("Category", {}).get("Name", "Unknown Category")
+    manufacturer = product.get("Manufacturer", {}).get("Name", "Unknown Manufacturer")
+    
+    # Use multiple attributes for the prompt
+    prompt = f"Provide usage advice for the following electronic component. Description: {description}. Category: {category}. Manufacturer: {manufacturer}. Suggest other components that may work well with it."
+    
     inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model.generate(inputs, max_length=100, num_beams=4, early_stopping=True)
+    outputs = model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)
     advice = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
     return advice
 
 # Streamlit app interface