Update app.py

app.py

@@ -1,4 +1,78 @@
 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
@@ -96,3 +170,5 @@ if user_input:
     # Update session state
     st.session_state.messages.append({"role": "user", "content": user_input})
     st.session_state.messages.append({"role": "assistant", "content": response})
+
+    '''