Adding app.py which contains the code written by the FLL holographic designers team

app.py

@@ -0,0 +1,176 @@
+import os
+import requests
+import base64
+
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import AIMessage, HumanMessage, SystemMessage
+import openai
+import gradio as gr
+
+
+# This was copy and pasted from https://www.gradio.app/guides/creating-a-chatbot-fast
+
+
+
+
+
+def predict(message, history, ingredients, servings, appliances, caloriesmin, caloriesmax, detected_ingredients, types_of_food, different_diets, cultures, additional_ingredients, pastry_or_not, openai_api_key):
+    llm = ChatOpenAI(temperature=1.0, openai_api_key=openai_api_key, model='gpt-3.5-turbo-0613', )
+
+    history_langchain_format = []
+    history_langchain_format.append(
+    SystemMessage(content=f"""
+                  Imagine that you are robust yet friendly chef that help new cooks cook.
+                  The cook that you are going help has {ingredients}, {detected_ingredients}, {additional_ingredients} and {appliances}.
+                  I am cooking for {servings} people. They want to cook this type of food : {pastry_or_not}.
+                  The number of calories in the dish should be in the range from {caloriesmin} to {caloriesmax}.
+                  The only categories of food it should use should be: {types_of_food}. 
+                  The user is on the following diets: {different_diets}.The dish must be from this culture: {cultures}. Give a small amount of background knowledge/where this dish came from.
+                  Recommend a good recipe when Rec Plz is typed that uses the ingredients, appliances on hand 
+                  but is also easy for beginners to cook. 
+                  """))
+                  
+                  
+
+    # this converts the history to langchain format 
+   
+    for human, ai in history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
+    
+    # this converts the message to langchain format
+    history_langchain_format.append(HumanMessage(content=message))
+    
+    # Calling chat gpt
+    gpt_response = llm(history_langchain_format)
+    
+    return gpt_response.content
+
+
+
+# def echo_image(input_image_filepath):
+#     #we copy and pasted this code from replicate
+#     print(input_image_filepath)
+    
+#     output = replicate.run(
+#         "kiransom/fll_detic:161277b70ee6ea38847ba2e1c56523dcdf77143ac029d52a795327c70404846e", # Model ID
+#         input={
+#             "image": open(input_image_filepath, "rb"),
+            
+#         }
+#     )
+#     print(output["output_path"])
+#     print(output["predictions_set"])
+#     return(output["output_path"], output["predictions_set"])
+    
+# Function to encode the image
+# Getting the base64 string
+# base64 is compact encoding of the bytes of the image
+def encode_image(image_path):
+  with open(image_path, "rb") as image_file:
+    return base64.b64encode(image_file.read()).decode('utf-8')
+
+
+def process_image(image_path, openai_api_key):
+    base64_image = encode_image(image_path)
+    question = "This is an image of ingredients available for cooking. Please list all the ingredients and approximate quantity of each ingredient in a numbered list."
+
+    headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {openai_api_key}"
+    }
+
+    payload = {
+    "model": "gpt-4-vision-preview",
+    "messages": [
+        {
+        "role": "user",
+        "content": [
+            {
+            "type": "text",
+            "text": question
+            },
+            {
+            "type": "image_url",
+            "image_url": {
+                "url": f"data:image/jpeg;base64,{base64_image}"
+            }
+            }
+        ]
+        }
+    ],
+    "max_tokens": 300
+    }
+
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+    print(response)
+
+    return response.json()["choices"][0]["message"]["content"]
+
+
+
+with gr.Blocks() as demo:
+
+    
+    with gr.Row():
+        openai_api_key = gr.Textbox(placeholder="Type in this box first.", label="Please enter your OpenAI key. If you do not have a key, please visit this site: https://platform.openai.com/signup/ . This textbox may duplicate. If so, DO NOT click the second textbox.")
+        
+
+    with gr.Row():
+        detected_ingredients = gr.Text()
+        gr.Interface(fn=process_image,
+               inputs=[gr.Image(width=400, height=400, type="filepath"), openai_api_key],
+                outputs=detected_ingredients
+            )
+    
+    with gr.Row():
+         ingredients = gr.CheckboxGroup(choices=["Salt", "Pepper", "Flour","Oil", "Pasta", "Rice", ], label="Common ingredients")
+    
+
+    with gr.Row():
+        appliances = gr.CheckboxGroup(choices=["stove", "blender", "oven", "pots", "air fryer", "pressure cooker", "microwave"], label="Appliances")
+
+    with gr.Row():
+        servings = gr.Slider(1, 20, step=1, label="Servings")
+        
+    with gr.Row():
+        cultures = gr.Radio(choices=["Italian", "French", "American", "Japanese", "Korean", "Chinese", "Jewish", "German", "Indian"], label="Cultures")           
+            
+    
+    with gr.Row():
+    
+        caloriesmin = gr.Slider(50, 2000, value=100, step=25, label="Calories Min")
+        caloriesmax = gr.Slider(100, 2000, value=1500, step=25, label="Calories Max")
+
+    with gr.Row():
+        types_of_food = gr.CheckboxGroup(choices=["fruits", "vegetables", "grains", "protein", "starch-rich food", "dairy", "fat",], label="Types of food you would like to include in your diet")
+
+    with gr.Row():
+            additional_ingredients = gr.Textbox(lines=2, label="Addtional Ingredients", placeholder="Please add any addtional ingredients the model missed.")
+
+    with gr.Row():
+        different_diets = gr.CheckboxGroup(choices=["Ketogenic Diet", "Meditarranean Diet", "Paleo Diet", "Whole30 Diet", "Vegan Diet", "Vegetarian Diet", "Raw Food Diet", "Ayurvedic Diet", "Carb Cycling", "Macrobiotic Diet"], label="Diets", info="Other - if you have another diet, please just enter the foods you are supposed to avoid into the Dietary Restrictions textbox and do not select this checkbox.")
+
+    with gr.Row():
+        pastry_or_not = gr.Radio(choices=["Pastry", "Other"], label="Pastry")
+
+    with gr.Row():
+        gr.ChatInterface(fn=predict,
+                        additional_inputs=[
+                            ingredients,
+                            servings, 
+                            appliances, 
+                            caloriesmin, 
+                            caloriesmax, 
+                            detected_ingredients, 
+                            types_of_food, 
+                            different_diets,
+                            cultures,
+                            additional_ingredients,
+                            pastry_or_not,
+                            openai_api_key
+                            ], 
+        )               
+
+        
+demo.launch(share=False)

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+openai
+langchain