Update app.py

app.py

@@ -38,4 +38,52 @@ def get_ingredients_bloom(food_name):
         response = pipe_bloom(prompt, max_length=50, num_return_sequences=1)
         generated_text = response[0]["generated_text"].strip()
         
-        # Post-process the response
+        # Post-process the response
+        ingredients = generated_text.split(":")[-1].strip()  # Handle cases like "Ingredients: ..."
+        ingredients = ingredients.replace(".", "").strip()  # Remove periods and extra spaces
+
+        # Validate the response to ensure no placeholders
+        if "ingredient1" in ingredients.lower() or "example" in ingredients.lower():
+            return "No valid ingredients found. Try again with a different food."
+
+        return ingredients
+    except Exception as e:
+        return f"Error generating ingredients: {e}"
+
+# Streamlit app setup
+st.title("Food Image Recognition with Ingredients")
+
+# Add banner image
+st.image("IR_IMAGE.png", caption="Food Recognition Model", use_column_width=True)
+
+# Sidebar for model information
+st.sidebar.title("Model Information")
+st.sidebar.write("**Image Classification Model**: Shresthadev403/food-image-classification")
+st.sidebar.write("**LLM for Ingredients**: bigscience/bloom-1b7")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose a food image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.write("Classifying...")
+
+    # Make predictions
+    predictions = pipe_classification(image)
+
+    # Display only the top prediction
+    top_food = predictions[0]['label']
+    st.header(f"Food: {top_food}")
+
+    # Generate and display ingredients for the top prediction
+    st.subheader("Ingredients")
+    try:
+        ingredients = get_ingredients_bloom(top_food)
+        st.write(ingredients)
+    except Exception as e:
+        st.error(f"Error generating ingredients: {e}")
+
+# Footer
+st.sidebar.markdown("Created with ❤️ using Streamlit and Hugging Face.")