Upload app.py

app.py

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+import numpy as np
+import gradio as gr
+import requests
+import json
+
+def list_to_dict(data):
+    results = {}
+
+    for i in range(len(data)):
+        # Access the i-th dictionary in the list using an integer index
+        d = data[i]
+        # Assign the value of the 'label' key to the 'score' value in the results dictionary
+        results[d['label']] = d['score']
+
+    # The results dictionary will now contain the label-score pairs from the data list
+    return results
+
+API_URL = "https://api-inference.huggingface.co/models/nateraw/food"
+headers = {"Authorization": "Bearer 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"}
+
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.request("POST", API_URL, headers=headers, data=data)
+    output = json.loads(response.content.decode("utf-8"))
+    return list_to_dict(output),json.dumps(output, indent=2, sort_keys=True)
+
+def get_nutrition_info(food_name):
+    #Make request to Nutritionix API
+    response = requests.get(
+        "https://trackapi.nutritionix.com/v2/search/instant",
+        params={"query": food_name},
+        headers={
+            "x-app-id": "63a710ef",
+            "x-app-key": "3ddc7e3feda88e1cf6dd355fb26cb261"
+        }
+    )
+    #Parse response and return relevant information
+    data = response.json()
+    response = data["branded"][0]["photo"]["thumb"]
+
+    # Open the image using PIL
+
+    return {
+               "food_name": data["branded"][0]["food_name"],
+               "calories": data["branded"][0]["nf_calories"],
+               "serving_size": data["branded"][0]["serving_qty"],
+               "serving_unit": data["branded"][0]["serving_unit"],
+               #"images": data["branded"][0]["photo"]
+           },response
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("Food-Classification-Calorie-Estimation and Volume-Estimation")
+    with gr.Tab("Food Classification"):
+        text_input = gr.Image(type="filepath")
+        text_output = [gr.Label(num_top_classes=6),
+                       gr.Textbox()
+                       ]
+        text_button = gr.Button("Food Classification")
+    with gr.Tab("Food Calorie Estimation"):
+        image_input = gr.Textbox(label="Please enter the name of the Food you want to get calorie")
+        image_output = [gr.Textbox(),
+                        gr.Image(type="filepath")
+                        ]
+        image_button = gr.Button("Estimate Calories!")
+    with gr.Tab("Volume Estimation"):
+        _image_input = gr.Textbox(label="Please enter the name of the Food you want to get calorie")
+        _image_output = [gr.Textbox(),
+                         gr.Image()
+                         ]
+        _image_button = gr.Button("Volume Calculation")
+    with gr.Tab("Future Works"):
+        gr.Markdown("Future work on Food Classification")
+        gr.Markdown(
+            "Currently the Model is trained on food-101 Dataset, which has 100 classes, In the future iteration of the project we would like to train the model on UNIMIB Dataset with 256 Food Classes")
+        gr.Markdown("Future work on Volume Estimation")
+        gr.Markdown(
+            "The volume model has been trained on Apple AR Toolkit and thus can be executred only on Apple devices ie a iOS platform, In futur we would like to train the volume model such that it is Platform independent")
+        gr.Markdown("Future work on Calorie Estimation")
+        gr.Markdown(
+            "The Calorie Estimation currently relies on Nutritionix API , In Future Iteration we would like to build our own Custom Database of Major Food Product across New York Restaurent")
+        gr.Markdown("https://github.com/Ali-Maq/Food-Classification-Volume-Estimation-and-Calorie-Estimation/blob/main/README.md")
+
+    text_button.click(query, inputs=text_input, outputs=text_output)
+    image_button.click(get_nutrition_info, inputs=image_input, outputs=image_output)
+    _image_button.click(get_nutrition_info, inputs=_image_input, outputs=_image_output)
+    with gr.Accordion("Open for More!"):
+        gr.Markdown("🍎 Designed and built by Ali Under the Guidance of Professor Dennis Shasha")
+        gr.Markdown("Contact me at ali.quidwai@nyu.edu 😊")
+
+demo.launch(share=True, debug=True)