testing the addition of model

app.py

@@ -1,20 +1,138 @@
 import streamlit as st
-from transformers import pipeline
-from PIL import Image
+import cv2
+import os
+from backend import *
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+# Create a folder to save captured images
+if not os.path.exists("captured_images"):
+    os.makedirs("captured_images")
 
-st.title("Hot Dog? Or Not?")
+# Initialize the session state
+session_state = st.session_state
+if 'ingredientsList' not in session_state:
+    session_state['ingredientsList'] = ["apple", "banana", "orange", "strawberries"]
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+def main():
+    
+    st.title('🧑🏽‍🍳 RecipeBud')
+    
+    st.sidebar.header('Ingredients & Nutrition')
+    # List of items
+    #items = ['Item 1', 'Item 2', 'Item 3']
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
+    #list to of Ingredients camptured
+    #ingredientsList =["apple", "orange", "mango"] #list()
+   
+    # Create a VideoCapture object to access the webcam
+    cap = cv2.VideoCapture(0)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+    # Set the video frame width and height (optional)
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 400)
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    # Check if the webcam is opened correctly
+    if not cap.isOpened():
+        st.error("Error: Unable to access the webcam.")
+        return
+
+    # Display a placeholder for the video stream
+    video_placeholder = st.empty()
+    # Button to capture image
+    if st.button("Capture Image"):
+        image_path = capture_image()
+        classification = classifyImage(image_path)
+        session_state['ingredientsList'].append(classification)
+
+    # Button to indicate done
+    #done_button = st.sidebar.button('Done')
+
+    # Display the captured ingredients
+    #st.write("Captured Ingredients:", session_state['ingredientsList'])
+
+    button_clicked = st.sidebar.button('Done')
+    
+    # Display recipes if "Done" is clicked
+    while not button_clicked:
+    # Read a frame from the webcam
+        ret, frame = cap.read()
+
+        if not ret:
+            st.error("Error: Unable to read frame from the webcam.")
+            break
+
+        # Display the frame in the Streamlit app
+        video_placeholder.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), channels="RGB", use_column_width=True)
+    if button_clicked:
+        cap.release()
+        if session_state['ingredientsList']:
+            session_state['ingredientsList'].pop()
+        #st.write("Updated Ingredients List:", session_state['ingredientsList'])
+
+        displayRecipes(session_state['ingredientsList'])
+        # Define content for each item
+        content = {}
+        for ingredient in session_state['ingredientsList']:
+            content[ingredient] = askGPT(f"Give me your estimate the calories, grams of protein, grams of sugar, grams of fat, and grams of carbohydrates per 100g of {ingredient} as a list")
+
+        # Display expanders for each item
+        for ingredient in session_state['ingredientsList']:
+            with st.sidebar.expander(ingredient):
+                st.write(content[ingredient])
+        displayRecipes(session_state['ingredientsList'])
+
+def displayRecipes(ingredientsList):
+    items = []
+    #now we are gonna send the ingredient list to ask gpt
+    prompt = f"I have following Ingredients :{','.join(ingredientsList)}. What can I make with these \
+            Ingredients? Give me A list of detailed recipes with measurements containing these ingredients with Nutrition Facts per 100g based on the widely accepted nutritional value of each of these ingredients. Rank the list from \
+            highest nutritional value to lowest. Give me results in \
+            following format and do not deviate from this format:\
+            ['Recipe Title', 'content of recipe and nutritional facts per 100g']. Only give me the list. Do not add commentary or personalized responses. Keep it under 200 words."
+    #prompt = f"You are going to act as a nutritional expert who has a lot of knowledge about food. I have the following ingredients: {','.join(ingredientsList)}. What can I make with these ingredients? Give me a list of names of recipes, maximum five."
+    LLMResult = askGPT(prompt)
+    lystOfRecipes = LLMResult.split('\n\n')
+    # print(lystOfRecipes)
+    for recipe in range(1,len(lystOfRecipes)-1):
+        items.append({"title": lystOfRecipes[recipe].split(":")[0], "content": ""})
+    # Display the items with =expanding boxes
+    for item in items:
+        #for side bar's item
+        #with st.sidebar.expander(item):
+            #st.write("man-holding-banana.jpeg")
+        #main page items
+        with st.expander(item["title"]):
+            st.write(askGPT(f"Give me a detailed recipe for a dish called {item['title']} containing all of the following ingredients: {','.join(ingredientsList)}. Make sure your response is easy to follow and comprehensive."))
+    
+
+def capture_image():
+    # Create a VideoCapture object to access the webcam
+    cap = cv2.VideoCapture(0)
+
+    # Set the video frame width and height (optional)
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 400)
+
+    # Check if the webcam is opened correctly
+    if not cap.isOpened():
+        st.error("Error: Unable to access the webcam.")
+        return
+
+    # Read a frame from the webcam
+    ret, frame = cap.read()
+
+    if not ret:
+        st.error("Error: Unable to read frame from the webcam.")
+        return
+
+    # Save the frame as an image
+    image_path = f"captured_images/captured_image_{len(os.listdir('captured_images')) + 1}.jpg"
+    cv2.imwrite(image_path, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    st.success(f"Image captured and saved as {image_path}")
+
+    # Release the VideoCapture and close the OpenCV window
+    cap.release()
+    return image_path
+
+
+if __name__ == '__main__':
+    main()

backend.py

@@ -0,0 +1,38 @@
+# This is the main logic file that contains hugging face model interaction
+
+# This model is for detecting food in the image.
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+import os
+import openai
+openai.organization = "org-5Z0c3Uk1VG7t3TsczN6M4FCi"
+#openai.api_key = os.getenv("OPENAI_API_KEY")
+openai.api_key_path ="./key.txt" 
+
+def askGPT(prompt="what can I make with potato?"):
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {
+          "role": "system",
+          "content":prompt 
+        },
+        {
+          "role": "user",
+          "content": ""
+        }      ],
+      temperature=1,
+      max_tokens=256,
+      top_p=1,
+      frequency_penalty=0,
+      presence_penalty=0
+    )
+    result = response["choices"][0]["message"]["content"]
+    return result
+
+def classifyImage(image):
+    pipe = pipeline("image-classification", model="microsoft/resnet-50")
+    result = pipe(image)
+    return result[0]['label']
+
+

requirements.txt

@@ -1,2 +1,46 @@
-transformers
-torch
+async-generator==1.10
+attrs==21.4.0
+certifi==2022.6.15
+cffi==1.15.0
+charset-normalizer==3.1.0
+cryptography==36.0.1
+distlib==0.3.4
+filelock==3.7.0
+fsspec==2023.9.1
+h11==0.13.0
+huggingface-hub==0.17.1
+idna==3.3
+numpy==1.22.3
+outcome==1.1.0
+packaging==23.0
+pandas==1.4.1
+pbr==5.9.0
+platformdirs==2.5.2
+pycparser==2.21
+pyOpenSSL==21.0.0
+python-dateutil==2.8.2
+pytz==2022.1
+PyYAML==6.0
+regex==2023.8.8
+requests==2.28.2
+safetensors==0.3.3
+scipy==1.9.3
+selenium==4.1.0
+six==1.16.0
+snakeviz==2.1.1
+sniffio==1.2.0
+sortedcontainers==2.4.0
+stevedore==3.5.0
+thread6==0.2.0
+tokenizers==0.13.3
+tornado==6.2
+tqdm==4.65.0
+transformers==4.33.2
+trio==0.19.0
+trio-websocket==0.9.2
+typing_extensions==4.5.0
+urllib3==1.26.8
+virtualenv==20.14.1
+virtualenv-clone==0.5.7
+virtualenvwrapper==4.8.4
+wsproto==1.0.0