Update app.py

app.py

@@ -1,78 +1,4 @@
 import streamlit as st
-from huggingface_hub import InferenceClient
-import os
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-api_key = os.getenv("api_key")
-
-# App title and description
-st.title("I am Your GrowBuddy 🌱")
-st.write("Let me help you start gardening. Let's grow together!")
-
-# Initialize Hugging Face InferenceClient
-model_name = "unsloth/gemma-2-2b"  # Use the appropriate model
-client = InferenceClient(model=model_name, token=api_key)
-
-# Initialize session state messages
-if "messages" not in st.session_state:
-    st.session_state.messages = [
-        {"role": "assistant", "content": "Hello there! How can I help you with gardening today?"}
-    ]
-
-# Display conversation history
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
-
-# Create a text area to display logs
-log_box = st.empty()
-
-# Function to generate response using Hugging Face's remote model
-def generate_response(prompt):
-    try:
-        log_box.text_area("Debugging Logs", "Generating output...", height=200)
-
-        # Generate output from the Hugging Face API
-        response = client.text_generation(prompt, max_new_tokens=100, temperature=0.7, do_sample=True)
-
-        # Print and log the response to understand the structure
-        log_box.text_area("Debugging Logs", f"Response: {response}", height=200)
-        
-        # Check for proper response structure
-        if isinstance(response, list) and len(response) > 0 and "generated_text" in response[0]:
-            output_text = response[0]["generated_text"]
-        else:
-            raise ValueError("Unexpected response structure from Hugging Face API.")
-        
-        log_box.text_area("Debugging Logs", f"Decoded response: {output_text}", height=200)
-        return output_text
-    except Exception as e:
-        st.error(f"Error during text generation: {str(e)}")
-        log_box.text_area("Error Details", str(e), height=200)
-        return "Sorry, I couldn't process your request."
-
-
-# User input field for gardening questions
-user_input = st.chat_input("Type your gardening question here:")
-
-if user_input:
-    with st.chat_message("user"):
-        st.write(user_input)
-
-    with st.chat_message("assistant"):
-        with st.spinner("Generating your answer..."):
-            response = generate_response(user_input)
-            st.write(response)
-
-    # Update session state
-    st.session_state.messages.append({"role": "user", "content": user_input})
-    st.session_state.messages.append({"role": "assistant", "content": response})
-
-
-'''
-import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import os
@@ -94,7 +20,7 @@ def load_model():
             return st.session_state.tokenizer, st.session_state.model
         else:
             tokenizer = AutoTokenizer.from_pretrained("KhunPop/Gardening")
-            model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b")
+            model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
             # Store the model and tokenizer in session state
             st.session_state.tokenizer = tokenizer
             st.session_state.model = model
@@ -171,4 +97,4 @@ if user_input:
     st.session_state.messages.append({"role": "user", "content": user_input})
     st.session_state.messages.append({"role": "assistant", "content": response})
 
-    '''
+