diff --git a/app/report_gen/page_6.html b/app/report_gen/page_6.html index 6383397..86b7939 100644 --- a/app/report_gen/page_6.html +++ b/app/report_gen/page_6.html @@ -76,7 +76,7 @@ {% if i < 5 %}
- {{ refeed_weekday_calories | default('1615KCals') }} + {{ refeed_weekday_calories | default('1615KCals') }} KCals
{{ refeed_weekday_protein | default('120g Protein') }}
@@ -88,7 +88,7 @@ {% else %}
- {{ refeed_weekend_calories | default('2000KCals') }} + {{ refeed_weekend_calories | default('2000KCals') }} KCals
{{ refeed_weekend_protein | default('120g Protein') }}
diff --git a/app/report_gen/page_7.html b/app/report_gen/page_7.html index d934c08..556cf35 100644 --- a/app/report_gen/page_7.html +++ b/app/report_gen/page_7.html @@ -26,7 +26,7 @@

Indications

-

{{ indication }}

+

{{ indication | default('No Respiratory Capacity Limitations')}}

diff --git a/app/services/__pycache__/graph_generator.cpython-312.pyc b/app/services/__pycache__/graph_generator.cpython-312.pyc index bf6ca27..2508cf6 100644 Binary files a/app/services/__pycache__/graph_generator.cpython-312.pyc and b/app/services/__pycache__/graph_generator.cpython-312.pyc differ diff --git a/app/services/__pycache__/report_generator.cpython-312.pyc b/app/services/__pycache__/report_generator.cpython-312.pyc index 4e782b3..8deea63 100644 Binary files a/app/services/__pycache__/report_generator.cpython-312.pyc and b/app/services/__pycache__/report_generator.cpython-312.pyc differ diff --git a/app/services/graph_generator.py b/app/services/graph_generator.py index edde5c2..89f29c3 100644 --- a/app/services/graph_generator.py +++ b/app/services/graph_generator.py @@ -1124,80 +1124,163 @@ class GraphGenerator: return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path) def generate_metabolism_chart( - self, rmr_kcal: float, save_as_base64: bool = True + self, + rmr_kcal: float, + weight_kg: float = None, + height_cm: float = None, + age_years: int = None, + sex: str = None, + save_as_base64: bool = True, ) -> str: """ Generate metabolism chart (Slow vs Fast Metabolism). + Matches the notebook implementation with ratio-based scale (0.3 to 1.9). Args: - rmr_kcal: Resting metabolic rate in kcal/day + rmr_kcal: Resting metabolic rate in kcal/day (measured RMR) + weight_kg: Weight in kg (optional, for calculating ratio) + height_cm: Height in cm (optional, for calculating ratio) + age_years: Age in years (optional, for calculating ratio) + sex: Sex ("male" or "female", optional, for calculating ratio) save_as_base64: If True, return base64 string, else return file path Returns: Base64 string or file path """ - from matplotlib.patches import FancyBboxPatch + from matplotlib.patches import Rectangle - fig, ax = plt.subplots(figsize=(10, 2.5)) + fig, ax = plt.subplots(figsize=(11.5, 2.5)) - # Chart data and positions - # Use normalized positions (0-100 scale) for uniform bar length - categories = ["Very Slow", "Slow", "Average", "Fast", "Very Fast"] - positions = [10, 30, 50, 70, 90] # Normalized positions on 0-100 scale + # Calculate ratio if we have all required parameters + ratio = None + if all([weight_kg, height_cm, age_years, sex]): + # Mifflin-St Jeor equation + if sex.lower() == "male": + mifflin_rmr = 10 * weight_kg + 6.25 * height_cm - 5 * age_years + 5 + elif sex.lower() == "female": + mifflin_rmr = 10 * weight_kg + 6.25 * height_cm - 5 * age_years - 161 + else: + mifflin_rmr = None - # Normalize the kcal value to 0-100 scale (assuming range 0-9000 kcal) - max_kcal = 9000 - normalized_value = (rmr_kcal / max_kcal) * 100 - indicator_pos = normalized_value - highlight_end = normalized_value + if mifflin_rmr and mifflin_rmr > 0: + ratio = rmr_kcal / mifflin_rmr - # Main Bar (Background) - using 0-100 scale - main_bar = FancyBboxPatch( - (0, 0.4), - 100, - 0.2, - boxstyle="round,pad=0,rounding_size=0.1", - ec="none", - fc="#E0E0E0", + # Bar setup - using ratio scale from 0.3 to 1.9 (as in notebook) + scale_edges = [0.3, 0.7, 0.9, 1.1, 1.3, 1.5, 1.9] + scale_labels = ["Very Slow", "Slow", "Average", "Fast", "Very Fast"] + tick_edges = scale_edges[1:-1] # Remove first and last tick (omit 0.3 and 1.9) + + x_start = scale_edges[0] + x_end = scale_edges[-1] + # Make the bar THICKER by increasing bar_height and adjusting y_bar + bar_height = 0.36 + y_bar = 0.48 + + color_before = "#B2FFC8" + color_after = "#ECEDF2" + gray_color = "#606060" + + # If we have a ratio, use it; otherwise map rmr_kcal to the scale + if ratio is not None: + highlight_end = min(max(ratio, x_start), x_end) + else: + # Fallback: map rmr_kcal to scale (assuming typical range 1000-3000 kcal/day) + # Map to 0.3-1.9 scale + min_rmr = 1000 + max_rmr = 3000 + normalized = (rmr_kcal - min_rmr) / (max_rmr - min_rmr) + highlight_end = x_start + normalized * (x_end - x_start) + highlight_end = min(max(highlight_end, x_start), x_end) + + # Draw plain rectangle bar (no rounding) + ax.add_patch( + Rectangle( + (x_start, y_bar), + x_end - x_start, + bar_height, + ec="none", + fc=color_after, + lw=0, + ) ) - ax.add_patch(main_bar) - # Highlighted Bar - highlight_bar = FancyBboxPatch( - (0, 0.4), - highlight_end, - 0.2, - boxstyle="round,pad=0,rounding_size=0.1", - ec="none", - fc="#B2FFC8", - ) - ax.add_patch(highlight_bar) + # Highlighted rectangle + if highlight_end > x_start: + ax.add_patch( + Rectangle( + (x_start, y_bar), + highlight_end - x_start, + bar_height, + ec="none", + fc=color_before, + lw=0, + ) + ) - # Text and Labels (show actual kcal value) + # kCals label, left-aligned, bold inside green, TEXT COLOR gray ax.text( - highlight_end / 2, - 0.5, - f"{rmr_kcal:.0f}kCals", - ha="center", + x_start + 0.07, + y_bar + bar_height / 2, + f"{int(round(rmr_kcal))}kCals", + ha="left", va="center", - color="#006400", + color=gray_color, + fontsize=12, + weight="bold", + bbox=dict(boxstyle="round,pad=0.14", ec="none", fc="#B2FFC8", alpha=1.0), + ) + + # Triangle marker above highlight end, gray + ax.plot( + [highlight_end], + [y_bar + bar_height + 0.08], + marker="v", + markersize=14, + color=gray_color, + clip_on=False, + ) + + # Draw ticks – omit leftmost/rightmost (thicker and below bar), color gray + tick_width = 4.1 + tick_bottom = y_bar - 0.07 # further below bar + tick_top = y_bar # at the base of bar + for edge in tick_edges: + ax.plot( + [edge, edge], + [tick_bottom, tick_top], + color=gray_color, + lw=tick_width, + solid_capstyle="butt", + clip_on=False, + zorder=2, + ) + + # Label locations (place directly under each tick), text color gray + label_y = tick_bottom - 0.08 + for label, tick in zip(scale_labels, tick_edges): + ax.text( + tick, + label_y, + label, + ha="center", + va="top", + fontsize=11, + weight="bold", + color=gray_color, + ) + + # Axis title: bold, with extra gap above the graph + ax.text( + x_start, + y_bar + bar_height + 0.5, + "Slow vs Fast Metabolism", + ha="left", + va="bottom", fontsize=14, weight="bold", ) - # Indicator Triangle - ax.plot(indicator_pos, 0.65, "v", markersize=15, color="#606060", clip_on=False) - - # Ticks and Labels - for pos, label in zip(positions, categories): - ax.text( - pos, 0.15, label, ha="center", va="center", fontsize=12, color="#333333" - ) - ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5) - - # Chart Styling - ax.set_title("Slow vs Fast Metabolism", fontsize=18, weight="bold", loc="left") - ax.set_xlim(0, 100) # Normalized scale for uniformity + ax.set_xlim(x_start, x_end) ax.set_ylim(0, 1) ax.axis("off") @@ -1214,6 +1297,7 @@ class GraphGenerator: ) -> str: """ Generate fuel source chart (Fats vs Carbs). + Matches the notebook implementation with proper tick styling. Args: fat_percentage: Fat percentage at rest @@ -1224,84 +1308,133 @@ class GraphGenerator: """ from matplotlib.patches import FancyBboxPatch - fig, ax = plt.subplots(figsize=(10, 2.5)) + fig, ax = plt.subplots(figsize=(11.5, 2.5)) carb_percentage = 100 - fat_percentage optimal_point = 75 - # Main Bars (Fats and Carbs) - # Fats bar (yellow) + # Let the bars be a bit thicker as well: increase bar height and y fats_bar = FancyBboxPatch( - (0, 0.4), + (0, 0.36), fat_percentage, - 0.2, + 0.28, boxstyle="round,pad=0,rounding_size=0.1", ec="none", fc="#FEEAAB", ) ax.add_patch(fats_bar) - # Carbs bar (blue) - starts where the fats bar ends carbs_bar = FancyBboxPatch( - (fat_percentage, 0.4), + (fat_percentage, 0.36), carb_percentage, - 0.2, + 0.28, boxstyle="round,pad=0,rounding_size=0.1", ec="none", fc="#A7F5FF", ) ax.add_patch(carbs_bar) - # Text and Labels + # Style: match font weight/color/size with other chart + label_fontprops = dict(fontsize=12, weight="bold", color="#333333") + ax.text( fat_percentage / 2, 0.5, - f"Fats\n{fat_percentage:.1f}%", + f"Fats\n{fat_percentage:.0f}%", ha="center", va="center", - color="#333333", - fontsize=12, - weight="bold", + **label_fontprops, ) ax.text( fat_percentage + carb_percentage / 2, 0.5, - f"Carbs\n{carb_percentage:.1f}%", + f"Carbs\n{100 - fat_percentage:.0f}%", ha="center", va="center", - color="#333333", - fontsize=12, - weight="bold", + **label_fontprops, ) # Add 'Optimal' label - ax.text(optimal_point, 0.75, "Optimal", ha="center", va="center", fontsize=12) - - # Indicator Triangle - ax.plot( - fat_percentage, 0.65, "v", markersize=15, color="#606060", clip_on=False + ax.text( + optimal_point, + 0.9, + "Optimal", + ha="center", + va="center", + fontsize=12, + weight="bold", + color="#606060", ) - # Ticks and Labels + # Optimal point line + ax.plot([optimal_point, optimal_point], [0.65, 0.8], color="#606060", lw=3) + + # Indicator Triangle + ax.plot(fat_percentage, 0.7, "v", markersize=15, color="#606060", clip_on=False) + + # Ticks and Labels - matching notebook implementation positions = [0, 25, 50, 75, 100] + tick_color = "#606060" for pos in positions: - ax.text( - pos, - 0.15, - str(pos), - ha="center", - va="center", - fontsize=12, - color="#333333", - ) - ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5) + # Smallest ticks (first and last) are thicker + if pos == 0: + ax.text( + pos + 0.5, + 0.15, + str(pos), + ha="center", + va="center", + fontsize=12, + color="#333333", + weight="bold", + ) + ax.plot( + [pos, pos], + [0.25, 0.37], + color=tick_color, + lw=14, + solid_capstyle="butt", + ) + elif pos == 100: + ax.text( + pos - 0.5, + 0.15, + str(pos), + ha="center", + va="center", + fontsize=12, + color="#333333", + weight="bold", + ) + ax.plot( + [pos, pos], + [0.25, 0.37], + color=tick_color, + lw=14, + solid_capstyle="butt", + ) + else: + ax.text( + pos, + 0.15, + str(pos), + ha="center", + va="center", + fontsize=12, + color="#333333", + weight="bold", + ) + ax.plot( + [pos, pos], + [0.25, 0.37], + color=tick_color, + lw=8, + solid_capstyle="butt", + ) - # Add a special tick for the 'Optimal' point - ax.plot([optimal_point, optimal_point], [0.6, 0.7], color="black", lw=2) - - # Chart Styling - ax.set_title("Fuel Source", fontsize=18, weight="bold", loc="left") - ax.set_xlim(0, 100) # Normalized scale for uniformity + # Chart Styling - uniform style for title + ax.set_title("Fuel Source", fontsize=14, weight="bold", loc="left", pad=22) + ax.set_xlim(0, 100) ax.set_ylim(0, 1) ax.axis("off") diff --git a/app/services/report_generator.py b/app/services/report_generator.py index f851277..6f211d3 100644 --- a/app/services/report_generator.py +++ b/app/services/report_generator.py @@ -524,9 +524,36 @@ class ReportGeneratorService: } rmr_metrics = temp_context_gen.calculate_rmr_and_fuel_source() - # Generate metabolism chart + # Convert height to cm if available + height_cm = None + height_str = patient_info.get("height", "") + if height_str: + try: + # Try to parse height string (e.g., "5'4"", "165cm", "165") + import re + # Check if it's in feet'inches" format + feet_inches_match = re.match(r"(\d+)'(\d+)\"", height_str) + if feet_inches_match: + feet = int(feet_inches_match.group(1)) + inches = int(feet_inches_match.group(2)) + height_cm = (feet * 12 + inches) * 2.54 + # Check if it ends with cm + elif "cm" in height_str.lower(): + height_cm = float(re.sub(r"[^\d.]", "", height_str)) + # Otherwise try to parse as number (assume cm) + else: + height_cm = float(re.sub(r"[^\d.]", "", height_str)) + except (ValueError, AttributeError): + pass + + # Generate metabolism chart with ratio calculation if we have all parameters metabolism_chart_b64 = self.graph_generator.generate_metabolism_chart( - rmr_metrics["rmr_kcal"], save_as_base64=True + rmr_metrics["rmr_kcal"], + weight_kg=weight_kg, + height_cm=height_cm, + age_years=patient_info.get("age", None), + sex=gender, + save_as_base64=True, ) graphs_dict["metabolism_chart"] = metabolism_chart_b64 diff --git a/notebooks/graphs.ipynb b/notebooks/graphs.ipynb index 6d043b7..c06ae99 100644 --- a/notebooks/graphs.ipynb +++ b/notebooks/graphs.ipynb @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 104, "id": "470e871e", "metadata": {}, "outputs": [ @@ -252,9 +252,9 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -311,7 +311,7 @@ " else:\n", " return 'Very Fast'\n", "\n", - "def plot_metabolism_chart(weight_kg, height_cm, age_years, sex, df):\n", + "def plot_metabolism_chart(weight_kg, height_cm, age_years, sex, df, fig_width=11.5, fig_height=2.5):\n", " \"\"\"\n", " Generates a 'Slow vs Fast Metabolism' chart styled to match the provided sample:\n", " - Bar is rectangular (not curved).\n", @@ -320,8 +320,9 @@ " - All text labels are bold.\n", " - (Modified) A gap is added between the title and the graph.\n", " - Text color, tick color, and triangle are all gray.\n", + " - (MODIFIED) The main horizontal bar is now THICKER.\n", " \"\"\"\n", - " fig, ax = plt.subplots(figsize=(10, 2.5))\n", + " fig, ax = plt.subplots(figsize=(fig_width, fig_height)) # set global uniform width\n", "\n", " # Identify resting phase and measured RMR\n", " rest_phase = df[df['MET'] <= 1.1] # assuming <1.1 MET = rest\n", @@ -337,8 +338,10 @@ "\n", " x_start = scale_edges[0]\n", " x_end = scale_edges[-1]\n", - " y_bar = 0.62\n", - " bar_height = 0.225\n", + " # ---- Make the bar THICKER by increasing bar_height and adjusting y_bar ----\n", + " bar_height = 0.36\n", + " y_bar = 0.48\n", + " # ---------------------------------------------------------------------------\n", "\n", " color_before = \"#B2FFC8\"\n", " color_after = \"#ECEDF2\"\n", @@ -415,12 +418,13 @@ " start += step\n", " return vals\n", "\n", - "def plot_fuel_source_chart():\n", + "def plot_fuel_source_chart(fig_width=11.5, fig_height=2.5):\n", " \"\"\"\n", " Generates and displays the 'Fuel Source' chart.\n", - " [unchanged code]\n", + " Uniform width/height for match.\n", " \"\"\"\n", - " fig, ax = plt.subplots(figsize=(10, 2.5))\n", + " fig, ax = plt.subplots(figsize=(fig_width, fig_height)) # uniform width/height\n", + "\n", " rest_phase = df[df['RER'] == 0.9]\n", " fat_rest = rest_phase['FAT(%)'].mean()\n", " carb_rest = rest_phase['CARBS(%)'].mean()\n", @@ -430,28 +434,32 @@ " carb_percentage = 100 - fat_percentage\n", " optimal_point = 75\n", "\n", + " # Let the bars be a bit thicker as well: increase bar height and y\n", " fats_bar = patches.FancyBboxPatch(\n", - " (0, 0.4), fat_percentage, 0.2,\n", + " (0, 0.36), fat_percentage, 0.28,\n", " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", " ec=\"none\", fc=\"#FEEAAB\",\n", " )\n", " ax.add_patch(fats_bar)\n", " carbs_bar = patches.FancyBboxPatch(\n", - " (fat_percentage, 0.4), carb_percentage, 0.2,\n", + " (fat_percentage, 0.36), carb_percentage, 0.28,\n", " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", " ec=\"none\", fc=\"#A7F5FF\",\n", " )\n", " ax.add_patch(carbs_bar)\n", "\n", + " # Style: match font weight/color/size with other chart\n", + " label_fontprops = dict(fontsize=12, weight='bold', color='#333333')\n", + "\n", " ax.text(fat_percentage / 2, 0.5, f'Fats\\n{fat_percentage:.0f}%', \n", - " ha='center', va='center', color='#333333', fontsize=12, weight='bold')\n", + " ha='center', va='center', **label_fontprops)\n", " ax.text(fat_percentage + carb_percentage / 2, 0.5, f'Carbs\\n{100-fat_percentage:.0f}%', \n", - " ha='center', va='center', color='#333333', fontsize=12, weight='bold')\n", + " ha='center', va='center', **label_fontprops)\n", "\n", - " ax.text(optimal_point, 0.9, 'Optimal', ha='center', va='center', fontsize=12)\n", - " ax.plot([optimal_point, optimal_point], [0.6, 0.8], color='#606060', lw=3)\n", + " ax.text(optimal_point, 0.9, 'Optimal', ha='center', va='center', fontsize=12, weight='bold', color='#606060')\n", + " ax.plot([optimal_point, optimal_point], [0.65, 0.8], color='#606060', lw=3)\n", "\n", - " ax.plot(fat_percentage, 0.65, 'v', markersize=15, color='#606060', clip_on=False)\n", + " ax.plot(fat_percentage, 0.7, 'v', markersize=15, color='#606060', clip_on=False)\n", "\n", " positions = [0, 25, 50, 75, 100]\n", " # Gray color for all ticks\n", @@ -459,15 +467,16 @@ " for pos in positions:\n", " # Smallest ticks (first and last)\n", " if pos == 0:\n", - " ax.text(pos + 0.5, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333')\n", - " ax.plot([pos, pos], [0.3, 0.4], color=tick_color, lw=14, solid_capstyle='butt')\n", + " ax.text(pos + 0.5, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333', weight='bold')\n", + " ax.plot([pos, pos], [0.25, 0.37], color=tick_color, lw=14, solid_capstyle='butt')\n", " elif pos == 100:\n", - " ax.text(pos - 0.5, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333')\n", - " ax.plot([pos, pos], [0.3, 0.4], color=tick_color, lw=14, solid_capstyle='butt')\n", + " ax.text(pos - 0.5, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333', weight='bold')\n", + " ax.plot([pos, pos], [0.25, 0.37], color=tick_color, lw=14, solid_capstyle='butt')\n", " else:\n", - " ax.text(pos, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333')\n", - " ax.plot([pos, pos], [0.3, 0.4], color=tick_color, lw=8, solid_capstyle='butt')\n", - " ax.set_title('Fuel Source', fontsize=18, weight='bold', loc='left')\n", + " ax.text(pos, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333', weight='bold')\n", + " ax.plot([pos, pos], [0.25, 0.37], color=tick_color, lw=8, solid_capstyle='butt')\n", + " # Uniform style for title\n", + " ax.set_title('Fuel Source', fontsize=14, weight='bold', loc='left', pad=22)\n", " ax.set_xlim(0, 100)\n", " ax.set_ylim(0, 1)\n", " ax.axis('off')\n",