Update app.py

app.py

@@ -15,8 +15,10 @@ st.write("Let me help you start gardening. Let's grow together!")
 # Function to load model
 def load_model():
     try:
-        tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-2-2b")
+        tokenizer = AutoTokenizer.from_pretrained("KhunPop/Gardening")
         model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b")
+        #tokenizer = AutoTokenizer.from_pretrained("KhunPop/Gardening", use_auth_token=api_key)
+        #model = AutoModelForCausalLM.from_pretrained("KhunPop/Gardening", use_auth_token=api_key)
         return tokenizer, model
     except Exception as e:
         st.error(f"Failed to load model: {e}")
@@ -43,17 +45,32 @@ for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.write(message["content"])
 
-# Function to generate response
+# Create a text area to display logs
+log_box = st.empty()
+
+# Function to generate response with debugging logs
 def generate_response(prompt):
     try:
         # Tokenize input prompt with dynamic padding and truncation
+        log_box.text_area("Debugging Logs", "Tokenizing the prompt...", height=200)
         inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device)
 
+        # Display tokenized inputs
+        log_box.text_area("Debugging Logs", f"Tokenized inputs: {inputs['input_ids']}", height=200)
+        
         # Generate output from model
+        log_box.text_area("Debugging Logs", "Generating output...", height=200)
         outputs = model.generate(inputs["input_ids"], max_new_tokens=100, temperature=0.7, do_sample=True)
 
+        # Display the raw output from the model
+        log_box.text_area("Debugging Logs", f"Raw model output (tokens): {outputs}", height=200)
+
         # Decode and return response
         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Display the final decoded response
+        log_box.text_area("Debugging Logs", f"Decoded response: {response}", height=200)
+        
         return response
     except Exception as e:
         st.error(f"Error during text generation: {e}")