Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from rembg import remove
+from PIL import Image
+from io import BytesIO
+import base64
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import random
+
+st.set_page_config(layout="wide", page_title="Image Classification App")
+st.write("## Image Food Classification App")
+st.sidebar.write("## Upload and download :gear:")
+
+# Initialize image classification and recipe generation models
+image_classifier = pipeline("image-classification", model="mjsp/sweet")
+recipe_model = GPT2LMHeadModel.from_pretrained("gpt2")
+recipe_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+MAX_FILE_SIZE = 5 * 1024 * 1024  # 5MB
+
+def convert_image(img):
+    buf = BytesIO()
+    img.save(buf, format="PNG")
+    byte_im = buf.getvalue()
+    return byte_im
+
+def fix_image(upload):
+    image = Image.open(upload)
+    col1.write("Original Image :camera:")
+    col1.image(image)
+
+    fixed = remove(image)
+    col2.write("Fixed Image :wrench:")
+    col2.image(fixed)
+    st.sidebar.markdown("\n")
+    st.sidebar.download_button("Download fixed image", convert_image(fixed), "fixed.png", "image/png")
+
+def generate_recipe(title, max_length=200):
+    matching_entries = [entry for entry in dataset if entry["title"] == title]
+
+    if matching_entries:
+        selected_entry = random.choice(matching_entries)
+        title = selected_entry["title"]
+        ingredients = selected_entry["ingredients"]
+    else:
+        title = "Default Recipe Title"
+        ingredients = []
+
+    input_text = f"Title: {title}\nIngredients: {', '.join(ingredients)}\n Instructions:"
+    input_ids = recipe_tokenizer.encode(input_text, return_tensors="pt")
+
+    output = recipe_model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+
+    generated_recipe = recipe_tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_recipe
+
+# Your dataset of titles, ingredients, and recipes
+dataset = [
+    {
+        "title": "Gulab Jamun",
+        "ingredients": ["milk powder", "ghee", "rose water", "saffron", "cardamom", "sugar syrup"],
+        "recipe": "Instructions for making Gulab Jamun..."
+    },
+    {
+        "title": "Jalebi",
+        "ingredients": ["all-purpose flour", "yogurt", "sugar", "water", "saffron strands", "cardamom powder", "ghee or oil for frying"],
+        "recipe": "Instructions for making Jalebi..."
+    },
+    {
+        "title": "Rasgulla",
+        "ingredients": ["milk", "sugar", "lemon juice", "rose water"],
+        "recipe": "Instructions for making Rasgulla..."
+    }
+]
+
+col1, col2 = st.columns(2)
+my_upload = st.sidebar.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+st.write()
+st.markdown("""---""")
+st.write()
+
+# global label
+# label = ""
+
+if my_upload is not None:
+    st.image(my_upload, caption="Uploaded Image", use_column_width=True)
+
+    if st.sidebar.button("Classify"):
+        st.sidebar.text("Classifying...")
+
+        image = Image.open(my_upload)
+
+        try:
+            classification_result = image_classifier(image)
+            top_prediction = classification_result[0]
+            label = top_prediction['label']
+            score = top_prediction['score']
+            st.sidebar.text("Top Prediction:")
+            st.sidebar.text(f"Label: {label}, Score: {score:.3f}")
+        except Exception as e:
+            st.error(f"Error during classification: {str(e)}")
+
+    if my_upload.size > MAX_FILE_SIZE:
+        st.error("The uploaded file is too large. Please upload an image smaller than 5MB.")
+    else:
+        fix_image(upload=my_upload)
+else:
+    fix_image("jalebi.jpg")
+
+
+
+# Recipe generation based on selected item
+st.write("## Recipe Generation")
+
+selected_item = st.selectbox("Select a food item", [entry["title"] for entry in dataset])
+if st.button("Generate Recipe"):
+     generated_recipe = generate_recipe(selected_item, max_length=200)
+     st.write(f"Recipe for {selected_item}:\n{generated_recipe}")
+
+# Add some descriptions and instructions
+st.sidebar.markdown("### Instructions")
+st.sidebar.markdown("1. Upload an image.")
+st.sidebar.markdown("2. Click the 'Classify' button to get the classification results.")
+st.sidebar.markdown("3. Select a food item to generate a recipe.")
+st.sidebar.markdown("4. Click the 'Generate Recipe' button to get the recipe.")
+
+# Display a placeholder for the main content
+st.write("Please upload an image and use the sidebar to classify it and generate a recipe.")