Create visualization/visualization.py

src/visualization/visualization.py

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+# © 2025 Elena Marziali — Code released under Apache 2.0 license.
+# See LICENSE in the repository for details.
+# Removal of this copyright is prohibited.
+
+# The system can analyze text and generate interactive visualizations
+# (e.g., bar charts, line plots, scatter plots) using Plotly.
+
+# === Function to generate the interactive chart ===
+def extract_numeric_values(text):
+    """ Extracts numeric ranges from the problem text. """
+    pattern = r"(\d+)\s*-\s*(\d+)|(\d+\.\d+|\d+)\s*(K|Pa|m/s)?"
+    matches = re.findall(pattern, text)
+
+    values = []
+    for match in matches:
+        if match[0] and match[1]:  # Range (300 - 600)
+            values.append((int(match[0]), int(match[1])))
+        elif match[2]:  # Single number with optional unit
+            values.append(float(match[2]))
+
+    return values if values else [1, 10]  # Default if no numbers found
+
+# Determines the most suitable chart type based on content
+def determine_chart_type(text):
+    text_lower = text.lower()
+    if re.search(r"(growth|decay|population)", text_lower):
+        return "exponential_growth"
+    elif re.search(r"(oscillation|frequency|wave)", text_lower):
+        return "sinusoidal"
+    elif re.search(r"(temperature|pressure)", text_lower):
+        return "temperature_pressure"
+    elif re.search(r"(speed|time|acceleration)", text_lower):
+        return "motion"
+    elif "linear" in text_lower:
+        return "linear"
+    elif "logarithmic" in text_lower:
+        return "logarithmic"
+    elif "gaussian" in text_lower or "normal distribution" in text_lower:
+        return "gaussian"
+    else:
+        return "generic"
+
+# Extracts numeric values from text for visualization
+def extract_numeric_values(text):
+    numbers = [float(n) for n in re.findall(r"\d+(?:\.\d+)?", text)]
+    if len(numbers) >= 2:
+        return numbers[:2]
+    elif len(numbers) == 1:
+        return [numbers[0], numbers[0] + 10]
+    else:
+        return [1, 10]
+
+# Generates and saves the interactive chart
+# The chart is displayed in the notebook and also saved as a PNG image.
+def generate_interactive_chart(problem):
+    chart_type = determine_chart_type(problem)
+    start, end = extract_numeric_values(problem)
+    x = np.linspace(start, end, 100)
+    fig = go.Figure()
+
+    if chart_type == "exponential_growth":
+        y = np.exp(x / max(x))
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Exponential Growth"))
+    elif chart_type == "sinusoidal":
+        y = np.sin(x)
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Sinusoidal Wave"))
+    elif chart_type == "motion":
+        y = x ** 2
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Speed vs Time"))
+    elif chart_type == "linear":
+        y = x
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Linear Trend"))
+    elif chart_type == "logarithmic":
+        x_log = np.where(x <= 0, 1e-3, x)
+        y = np.log(x_log)
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Logarithmic"))
+    elif chart_type == "gaussian":
+        mu, sigma = np.mean(x), np.std(x)
+        y = np.exp(-((x - mu)**2) / (2 * sigma**2))
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Gaussian"))
+    else:
+        y = np.sin(x)
+        fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name="Generic"))
+
+    caption = f"Visualization of the '{chart_type}' model from {start} to {end} for the problem: \"{problem}\""
+    fig.update_layout(
+        title=caption,
+        xaxis_title="X Axis",
+        yaxis_title="Y Axis",
+        template="plotly_white"
+    )
+    fig.show()
+
+    fig.write_image("grafico_output.png", format="png", width=800, height=500)
+    print("Image saved as 'grafico_output.png'")
+    return fig, caption
+
+# === Run example chart ===
+example_problem = "growth"
+fig, caption = generate_interactive_chart(example_problem)