update bmi data structure

app.py

@@ -37,41 +37,85 @@ bmi_ranges_model2 = {
         "BMI 40.0 and above"
     ]
 }
+bmi_mapp = {
+    "BMI less than 16.0":"BMI < 16.0",
+        "BMI between 16.0 and 16.99":"16.0 ≤ BMI ≤ 16.99",
+        "BMI between 17.0 and 18.49":"17.0 ≤ BMI ≤ 18.49",
+    "BMI between 18.5 and 20.4":"18.5 ≤ BMI ≤ 20.4",
+        "BMI between 20.5 and 22.4":"20.5 ≤ BMI ≤ 22.4",
+        "BMI between 22.5 and 24.9":"22.5 ≤ BMI ≤ 24.9",
+    "BMI between 25.0 and 26.9":"25.0 ≤ BMI ≤ 26.9",
+        "BMI between 27.0 and 28.9":"27.0 ≤ BMI ≤ 28.9",
+        "BMI between 29.0 and 29.9":"29.0 ≤ BMI ≤ 29.9",
+    "BMI between 30.0 and 34.9": "30.0 ≤ BMI ≤ 34.9",
+        "BMI between 35.0 and 39.9": "35.0 ≤ BMI ≤ 39.9",
+        "BMI 40.0 and above":"BMI ≥ 40.0"
+}
+
+# Define BMI ranges with boundaries and midpoints for adjusted BMI calculation
+bmi_ranges = {
+    "underweight (x < 18.5 bmi)": {
+        "BMI < 16.0": (0, 16.0, 16.0),  # Upper boundary 16.0
+        "16.0 ≤ BMI ≤ 16.99": (16.0, 16.99, 16.5),  # Midpoint 16.5
+        "17.0 ≤ BMI ≤ 18.49": (17.0, 18.49, 17.75)  # Midpoint 17.75
+    },
+    "normal weight (18.5 < x < 25 bmi)": {
+        "18.5 ≤ BMI ≤ 20.4": (18.5, 20.4, 19.45),  # Midpoint 19.45
+        "20.5 ≤ BMI ≤ 22.4": (20.5, 22.4, 21.45),  # Midpoint 21.45
+        "22.5 ≤ BMI ≤ 24.9": (22.5, 24.9, 23.7)    # Midpoint 23.7
+    },
+    "overweight (25 bmi < x < 30)": {
+        "25.0 ≤ BMI ≤ 26.9": (25.0, 26.9, 25.95),  # Midpoint 25.95
+        "27.0 ≤ BMI ≤ 28.9": (27.0, 28.9, 27.95),  # Midpoint 27.95
+        "29.0 ≤ BMI ≤ 29.9": (29.0, 29.9, 29.45)   # Midpoint 29.45
+    },
+    "obesity (x > 30 bmi)": {
+        "30.0 ≤ BMI ≤ 34.9": (30.0, 34.9, 32.5),   # Midpoint 32.5
+        "35.0 ≤ BMI ≤ 39.9": (35.0, 39.9, 37.45),  # Midpoint 37.45
+        "BMI ≥ 40.0": (40.0, 100, 40.0)            # Lower boundary 40.0
+    }
+}
 
 def predict_bmi(image, height_in_inches):
     # Prepare inputs for Model 1
     inputs_model1 = processor(text=bmi_classes_model1, images=image, return_tensors="pt", padding=True)
     outputs_model1 = model(**inputs_model1)
     probs_model1 = outputs_model1.logits_per_image.softmax(dim=1)
+    print(probs_model1,'probs_model1')
     
     # Get the broad category prediction from Model 1
     max_prob_index_model1 = probs_model1.argmax().item()
     predicted_bmi_class = bmi_classes_model1[max_prob_index_model1]
+    print(predicted_bmi_class,'predicted_bmi_class')
 
     # Select class names for Model 2 based on Model 1's prediction
     model2_classes = bmi_ranges_model2[predicted_bmi_class.lower()]
+    print(model2_classes,'model2_classes')
     
     # Prepare inputs for Model 2
     inputs_model2 = processor(text=model2_classes, images=image, return_tensors="pt", padding=True)
     outputs_model2 = model(**inputs_model2)
     probs_model2 = outputs_model2.logits_per_image.softmax(dim=1)
+    print(probs_model2,'probs_model2')
 
     # Get the finer range prediction from Model 2
     max_prob_index_model2 = probs_model2.argmax().item()
     finer_bmi_range = model2_classes[max_prob_index_model2]
+    print(finer_bmi_range,'finer_bmi_range')
 
     # Determine the BMI prediction based on the range
     bmi_prediction = get_adjusted_bmi(predicted_bmi_class, finer_bmi_range)
+    print(bmi_prediction)
     
     # Calculate weight using user-provided height
-    predicted_weight = calculate_weight(bmi_prediction, height_in_inches)
+    #predicted_weight = calculate_weight(bmi_prediction, height_in_inches)
 
     # Create the JSON output
     result = {
         "weightCategory": f"{predicted_bmi_class} - {finer_bmi_range}",
         "bmiPrediction": f"{bmi_prediction:.2f}",
         "height": str(height_in_inches),
-        "predictedWeight": f"{predicted_weight:.2f} lbs"
+        #"predictedWeight": f"{predicted_weight:.2f} lbs"
     }
 
     return result
@@ -79,10 +123,12 @@ def predict_bmi(image, height_in_inches):
 def get_adjusted_bmi(weight_category, finer_range):
     """Return the appropriate BMI value for the given finer range within the weight category."""
     category_ranges = bmi_ranges.get(weight_category.lower())
+    print(category_ranges,'category_ranges')
     for range_label, (low, high, mid) in category_ranges.items():
+        print(range_label,'range_label')
         if "BMI <" in range_label or "BMI ≥" in range_label:
             return high if "BMI <" in range_label else low
-        elif range_label == finer_range:
+        elif range_label == bmi_mapp[finer_range]:
             return mid
     return None