Update app.py

app.py

@@ -73,7 +73,6 @@ def load_llm_model():
 
 llm_model = load_llm_model()
 
-# --- 7. Answer Greeting and Handle Q&A Queries ---
 def answer_question(query, context=""):
     greetings = ["hi", "hello", "hii", "hey", "greetings", "how are you", "what's up", "how's it going"]
     
@@ -106,22 +105,40 @@ def answer_question(query, context=""):
 
     return answer
 
-# --- 8. Classify Query Type (Q&A or Recipe Search) ---
-@st.cache_resource
-def load_classifier():
-    return pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-
-classifier = load_classifier()
-
-def classify_query(query):
-    recipe_keywords = ["make", "cook", "bake", "recipe", "prepare"]
-    if any(keyword in query.lower() for keyword in recipe_keywords):
-        return "Recipe Search"
+# --- 8. Few-Shot Classification Function ---
+def classify_with_few_shot(query):
+    prompt = """
+    Classify the following query as one of three types: 
+    1. Greeting 
+    2. Recipe Search 
+    3. Non-Recipe Query
     
-    labels = ["Q&A", "Recipe Search"]
-    result = classifier(query, candidate_labels=labels, multi_label=False)
-    return result.get("labels", ["Q&A"])[0]
-
+    Example Queries:
+    - "Hi" -> Greeting
+    - "How do I make lasagna?" -> Recipe Search
+    - "What is the capital of France?" -> Non-Recipe Query
+    
+    Query: "{query}"
+    Classification:
+    """
+    
+    full_prompt = prompt.format(query=query)
+    
+    # Use your existing LLM model (or another one) for the few-shot classification
+    response = llm_model(question=full_prompt, context="")  # Adjust to how your model processes prompts
+    
+    classification = response.get("answer", "").strip()  # Assuming the model returns the classification directly
+    
+    # Check the output and map it to a valid intent
+    if "Greeting" in classification:
+        return "Greeting"
+    elif "Recipe Search" in classification:
+        return "Recipe Search"
+    elif "Non-Recipe Query" in classification:
+        return "Non-Recipe Query"
+    else:
+        return "Unclassified"
+        
 # --- 9. Display Image Function ---
 def display_image(image_url, recipe_name):
     try:
@@ -136,7 +153,7 @@ def display_image(image_url, recipe_name):
         placeholder_url = "https://via.placeholder.com/300?text=No+Image"
         st.image(placeholder_url, caption=recipe_name, use_container_width=True)
 
-# --- 10. Streamlit UI ---
+# --- 10. Streamlit UI --- 
 st.title("🍽️ AI Recipe & Q&A Assistant")
 
 user_query = st.text_input("Enter your question or recipe search query:", "", key="main_query_input")
@@ -146,31 +163,27 @@ if "retrieved_recipes" not in st.session_state:
 
 if st.button("Ask AI"):
     if user_query:
-        # Handle greetings separately
-        greeting_response = answer_question(user_query)
-        if greeting_response == "Hello! How can I assist you today?":
+        # Classify query using few-shot prompting
+        intent = classify_with_few_shot(user_query)  # Updated to use classify_with_few_shot()
+
+        if intent == "Greeting":
             st.subheader("🤖 AI Answer:")
-            st.write(greeting_response)
-        else:
-            # Classify query
-            intent = classify_query(user_query)
-
-            if intent == "Q&A":
-                st.subheader("🤖 AI Answer:")
-                response = answer_question(user_query)
-                st.write(response)
-
-            elif intent == "Recipe Search":
-                retrieved_recipes = retrieve_recipes(user_query)
-                if retrieved_recipes is not None and not retrieved_recipes.empty:
-                    st.session_state["retrieved_recipes"] = retrieved_recipes
-                    st.subheader("🍴 Found Recipes:")
-                    for index, recipe in retrieved_recipes.iterrows():
-                        st.markdown(f"### {recipe['title']}")
-                        st.write(f"**Ingredients:** {recipe['ingredients']}")
-                        st.write(f"**Instructions:** {recipe['instructions']}")
-                        display_image(recipe.get('img_src', ''), recipe['title'])
-                else:
-                    st.warning("⚠️ No relevant recipes found.")
+            st.write(answer_question(user_query))
+
+        elif intent == "Recipe Search":
+            retrieved_recipes = retrieve_recipes(user_query)
+            if retrieved_recipes is not None and not retrieved_recipes.empty:
+                st.session_state["retrieved_recipes"] = retrieved_recipes
+                st.subheader("🍴 Found Recipes:")
+                for index, recipe in retrieved_recipes.iterrows():
+                    st.markdown(f"### {recipe['title']}")
+                    st.write(f"**Ingredients:** {recipe['ingredients']}")
+                    st.write(f"**Instructions:** {recipe['instructions']}")
+                    display_image(recipe.get('img_src', ''), recipe['title'])
             else:
-                st.warning("❌ Unable to classify the query.")
+                st.warning("⚠️ No relevant recipes found.")
+        elif intent == "Non-Recipe Query":
+            st.subheader("🤖 AI Answer:")
+            st.write("I'm specialized in recipes! Feel free to ask me anything food-related. 😊")
+        else:
+            st.warning("❌ Unable to classify the query.")