Create code(4)

code(4)

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import re
+
+# --- Backend Functions ---
+
+def initialize_gemini_api(api_key):
+    """Initializes the Gemini model and tokenizer."""
+    try:
+        # Using Auto Classes is generally recommended for loading from Hugging Face
+        tokenizer = AutoTokenizer.from_pretrained("google/gemini-1.5-pro-001", token=api_key) #check if model has a tokenizer and version number.
+        model = AutoModelForCausalLM.from_pretrained("google/gemini-1.5-pro-001", token=api_key, device_map="auto", torch_dtype=torch.bfloat16) #Added model device and dtype.
+
+        return model, tokenizer
+    except Exception as e:
+        st.error(f"Error initializing model: {e}")
+        return None, None
+
+
+def preprocess_input(user_input, input_type):
+    """Preprocesses the input based on the input type."""
+
+    prompt_templates = {
+        "recipe_suggestion": "I have the following ingredients: {}. Suggest a recipe, and the recipe must include the ingredients I provided. Provide steps",
+        "promotion_idea": "Suggest a promotion to increase customer engagement based on these goals/themes: {}.",
+        "waste_reduction_tip": "Suggest strategies, including numbered steps, to minimize food waste based on this context/these ingredients: {}.",
+        "event_planning": "I want to plan an event. Here's the description/goals/requirements: {}.  Give detailed, step-by-step instructions and important considerations.",
+    }
+
+    prompt = prompt_templates.get(input_type)
+    if prompt:
+        return prompt.format(user_input)
+    else:
+        return "Invalid input type."  # Should ideally never happen due to Streamlit UI controls.
+
+def generate_suggestion(model, tokenizer, processed_input):
+    """Generates text using the Gemini model."""
+
+    try:
+        input_ids = tokenizer(processed_input, return_tensors="pt").to(model.device) # Make sure tensors are on same device
+        outputs = model.generate(**input_ids, max_new_tokens=512, temperature=0.7, top_k=50, top_p=0.95, do_sample=True) # Added important params for generation quality
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return generated_text
+    except Exception as e:
+        st.error(f"Error during generation: {e}")
+        return "An error occurred during suggestion generation."
+
+
+def postprocess_output(raw_response, input_type):
+    """Postprocesses the generated text."""
+
+    # Remove any leading/trailing whitespace
+    cleaned_response = raw_response.strip()
+    # Further, specific postprocessing according to context
+    if input_type == 'recipe_suggestion':
+      try:
+        pass # Can add custom filtering
+      except:
+        pass
+
+    elif input_type == 'promotion_idea':
+      try:
+        pass #Can add custom regex and filters
+      except:
+        pass
+    elif input_type == "waste_reduction_tip" or input_type == 'event_planning':
+        try:
+           # Check to ensure instructions and steps in final output.
+          pass
+        except:
+            pass
+
+    # Basic example: Split into sentences for better readability (can be improved)
+    sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', cleaned_response)
+    formatted_response = "\n\n".join(sentences)
+    return formatted_response
+def get_ai_suggestion(user_input, input_type, api_key):
+    model, tokenizer = initialize_gemini_api(api_key)
+    if model is None or tokenizer is None:
+        return "Failed to initialize the model.  Check your API key."
+    processed_input = preprocess_input(user_input, input_type)
+    raw_response = generate_suggestion(model, tokenizer, processed_input)
+    formatted_response = postprocess_output(raw_response, input_type)
+    return formatted_response
+
+
+
+# --- Streamlit Frontend ---
+
+st.set_page_config(page_title="AI Restaurant Assistant", layout="wide") #Set page config
+st.sidebar.title("AI Restaurant Assistant")
+
+# --- API KEY HANDLING ---
+
+# Use st.session_state to persist the API key *only for the session*
+if 'api_key' not in st.session_state:
+    st.session_state.api_key = ''
+# IMPORTANT SECURITY NOTE:  This method is suitable for demonstration/local development.
+# For a production deployment, you MUST use a more secure method of storing the API key,
+# such as environment variables and NEVER hardcode it or commit it to version control.
+api_key_input = st.sidebar.text_input("Enter your Hugging Face API key:", type="password", value=st.session_state.api_key)
+
+if api_key_input:
+    st.session_state.api_key = api_key_input #Value is automatically cached and input bar has api_key once entered.
+
+if not st.session_state.api_key:
+    st.sidebar.warning("Please enter your Hugging Face API key to use the application.")
+    st.stop()  # Stop execution if no API key
+
+# --- Input Selection ---
+input_type = st.sidebar.selectbox("What kind of suggestion do you need?",
+                                  ["recipe_suggestion", "promotion_idea", "waste_reduction_tip", "event_planning"])
+
+# --- Main Area ---
+st.title("Get AI-Powered Suggestions")
+st.write("This tool leverages the power of the Gemini 1.5 Pro model to assist with various restaurant management tasks.") # Introduction and description
+user_input = st.text_area("Enter your input here:", height=150, key="user_input") #Key is added
+
+if st.button("Generate Suggestion"):
+    if user_input:
+        with st.spinner("Generating suggestion..."):
+            suggestion = get_ai_suggestion(user_input, input_type, st.session_state.api_key)
+            st.markdown("### AI Suggestion:", unsafe_allow_html=True) #Style output and enhance it visually.
+            st.write(suggestion) #Can upgrade output design by having boxes etc.
+    else:
+        st.warning("Please enter some input.")