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app.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.schema import HumanMessage, SystemMessage
+
+from langchain_community.tools import DuckDuckGoSearchRun
+from ai71 import AI71
+import gradio as gr
+import openai 
+import os
+import re
+import matplotlib.pyplot as plt
+
+from PIL import Image
+import numpy as np
+import pytesseract
+# Make sure to import the necessary OpenAI API client and configure it.
+all_cals = {}
+def extract_calories_and_items(text):
+    # Use regular expression to find all numerical values associated with "calory" or "calories"
+    pattern = r'(\d+)\s*(?:calory|calories)'
+    matches = re.findall(pattern, text, re.IGNORECASE)
+    
+    # Convert the matches to integers
+    calories = [int(match) for match in matches]
+    
+    return calories
+
+def plot_calories(calories):
+    labels = sorted(calories, key=calories.get)
+    vals = [calories[label] for label in labels]
+    plt.barh(labels, vals, color='skyblue')
+    plt.xlabel('Calories')
+    plt.title('Item and Count')
+    plt.tight_layout()
+    
+def parse_items(items_string):
+    # Remove square brackets and split by comma
+    items_list = items_string.strip('[]').split(',')
+    
+    item_dict = {}
+    
+    # Define the pattern to match the quantity and item
+    pattern = r'(\d+)\s*x\s*(\w+)'
+    
+    for item in items_list:
+        match = re.match(pattern, item.strip())
+        if match:
+            quantity = int(match.group(1))
+            item_name = match.group(2)
+            if item_name in item_dict:
+                item_dict[item_name] += quantity
+            else:
+                item_dict[item_name] = quantity
+    
+    return item_dict
+
+# Set the API key for AI71
+AI71_API_KEY = "key"
+AI71_BASE_URL = "https://api.ai71.ai/v1/"
+client = AI71(AI71_API_KEY)
+
+search = DuckDuckGoSearchRun()
+
+# usr_input = input(f"User:")
+# 
+# print(items)
+
+
+
+def chatGPT_food(userinput, temperature=0.1, max_tokens=300):
+
+    keyword = client.chat.completions.create(
+        model="tiiuae/falcon-180B-chat",
+        messages=[
+            {"role": "system", "content": '''you need to extract the food item from the user text without any comments
+             example: 
+             user: I ate two apples
+             assistant: 2 x apple'''},
+            {"role": "user", "content": userinput}
+        ],
+        # temperature=0.5, 
+    )
+
+    items = parse_items(keyword.choices[0].message.content)
+    
+    for item, count in items.items():
+        result = search.invoke(f'calories of {item}')
+
+        response = client.chat.completions.create(
+                model="tiiuae/falcon-180B-chat",
+                messages=[
+                    {"role": "system", "content": '''based on the provided information extract the calories count per portion of the item provided, just the calories and portion in grams or ml without further comments 
+                        Example:
+                        orange 47 calories per 100 gram
+                        cola 38 calories per 100 gram
+                        do not generate more or add any unneeded comments, just follow the examples strictly'''},
+                    {"role": "user", "content": result}
+                ],
+                temperature=0.2, 
+            )
+
+    # print("search")
+    # print(result)
+    # print("ai")
+    # print (response.choices[0].message.content)
+        calories = extract_calories_and_items(response.choices[0].message.content)
+        # print("calories")
+        # print(calories)
+        try:
+            all_cals[f"{count}x{item}"] = count*calories[0]
+        except:
+            continue
+        return all_cals
+
+def chatGPT_invoice(userinput, temperature=0.1, max_tokens=300): 
+    response = client.chat.completions.create(
+        model="tiiuae/falcon-180B-chat",
+        messages=[
+            {"role": "system", "content": '''from the following invoice, find the name of the restaurant, then write a table for each food in the invoice and estimate its calories count only knowing that this food is from the same restaurant, with no further text or comments, or notes:
+            example:
+            "Restaurant: KFC
+            <insert the table of food and estimated calories>"
+            Do it for this text:'''},
+            {"role": "user", "content": userinput}
+        ],
+        temperature=temperature, 
+        max_tokens=max_tokens
+    )
+    return response.choices[0].message.content
+  
+def update_plot(userinput):
+    # all_cals = chatGPT_food(userinput)
+    fig, ax = plt.subplots()
+    plot_calories(all_cals)
+    return fig
+
+def ocr(input_img):
+    img1 = np.array(input_img)
+    text = pytesseract.image_to_string(img1)
+    output = chatGPT_invoice(text)
+    return output
+
+with gr.Blocks() as demo:
+    with gr.Tab("Food Calories"):
+        food = gr.Textbox(label="Food")
+        output = gr.Textbox(label="Calories")
+        greet_btn = gr.Button("Get Calories")
+        greet_btn.click(fn=chatGPT_food, inputs=food, outputs=output)
+
+    with gr.Tab("Invoice OCR"):
+        image_input = gr.Image(height=200, width=200)
+        output_text = gr.Textbox(label="Estimated Calories from Invoice")
+        demo_ocr = gr.Interface(fn=ocr, inputs=image_input, outputs=output_text)
+
+    with gr.Tab("Calories Plot"):
+        # food_plot = gr.Textbox(label="Enter Food for Plot")
+        plot_output = gr.Plot(label="Calories Plot")
+        plot_btn = gr.Button("Generate Plot")
+        plot_btn.click(fn=update_plot, inputs=plot_btn, outputs=plot_output)
+
+demo.launch()

requirements.txt

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+openai
+langchain
+gradio
+ai71
+pytesseract
+matplotlib
+langchain-community