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reports/progress_report.py

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+import sqlite3
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from reportlab.lib.pagesizes import A4
+from reportlab.pdfgen import canvas
+from reportlab.lib.units import inch
+import os
+import tempfile
+import streamlit as st
+import numpy as np
+
+def show_progress_report(name):
+    st.title("📈 Weekly Progress Report")
+
+    # Connect to DB and fetch session IDs
+    conn = sqlite3.connect("data/mindmentor.db")
+    df_sess = pd.read_sql_query("SELECT * FROM user_sessions WHERE name = ?", conn, params=(name,))
+    session_ids = tuple(df_sess["id"].tolist())
+
+    if not session_ids:
+        st.warning("No session data found.")
+        return
+
+    def tuple_to_sql(tup):
+        return f"({','.join(str(x) for x in tup)})"
+
+    session_ids_sql = tuple_to_sql(session_ids)
+
+    # Fetch logs
+    df_cog = pd.read_sql_query(f"SELECT * FROM cognitive_logs WHERE session_id IN {session_ids_sql}", conn)
+    df_pers = pd.read_sql_query(f"SELECT * FROM personality_logs WHERE session_id IN {session_ids_sql}", conn)
+    df_ei = pd.read_sql_query(f"SELECT * FROM ei_logs WHERE session_id IN {session_ids_sql}", conn)
+    conn.close()
+
+    # ---------------- COGNITIVE SUMMARY ----------------
+    st.subheader("🧠 Cognitive Task Summary")
+    cog_chart_path = None
+    if not df_cog.empty:
+        # Exclude unwanted tasks
+        df_cog = df_cog[~df_cog["task_name"].isin(["stroop_test", "symbol_match"])]
+        if not df_cog.empty:
+            cog_summary = df_cog.groupby("task_name").agg({
+                "score": "mean",
+                "reaction_time": "mean"
+            }).round(2)
+            st.dataframe(cog_summary)
+
+            fig1, ax1 = plt.subplots()
+            cog_summary["score"].plot(kind="bar", ax=ax1, color="lightblue")
+            ax1.set_title("Cognitive Task Scores")
+            ax1.set_ylabel("Score")
+            plt.tight_layout()
+            cog_chart_path = os.path.join(tempfile.gettempdir(), "cog_chart.png")
+            plt.savefig(cog_chart_path)
+            st.image(cog_chart_path)
+        else:
+            cog_summary = pd.DataFrame()
+            st.info("No valid cognitive task data (after filtering).")
+    else:
+        cog_summary = pd.DataFrame()
+        st.info("No cognitive data found.")
+
+    # ---------------- PERSONALITY RADAR ----------------
+    st.subheader("🧬 Personality Trait Summary")
+    radar_path = None
+    if not df_pers.empty:
+        pers_summary = df_pers.groupby("trait").agg({"score": "mean"}).round(2)
+        st.dataframe(pers_summary)
+
+        fig2 = plt.figure(figsize=(5, 5))
+        categories = list(pers_summary.index)
+        values = pers_summary["score"].tolist()
+        values += values[:1]
+        angles = [n / float(len(categories)) * 2 * np.pi for n in range(len(categories))]
+        angles += angles[:1]
+        ax2 = plt.subplot(111, polar=True)
+        ax2.plot(angles, values, linewidth=2, linestyle='solid')
+        ax2.fill(angles, values, 'skyblue', alpha=0.4)
+        ax2.set_xticks(angles[:-1])
+        ax2.set_xticklabels(categories)
+        ax2.set_title("Big Five Personality Traits")
+        radar_path = os.path.join(tempfile.gettempdir(), "radar_chart.png")
+        plt.tight_layout()
+        plt.savefig(radar_path)
+        st.image(radar_path)
+    else:
+        pers_summary = pd.DataFrame()
+        st.info("No personality data found.")
+
+    # ---------------- EI DRILL HEATMAP ----------------
+    st.subheader("💖 EI Drill Activity")
+    heatmap_path = None
+    if not df_ei.empty:
+        ei_counts = df_ei.groupby("drill_type").size().reset_index(name="Count")
+        heatmap_data = ei_counts.pivot_table(index="drill_type", values="Count")
+
+        fig3, ax3 = plt.subplots(figsize=(4, 3))
+        sns.heatmap(heatmap_data, annot=True, cmap="YlGnBu", ax=ax3)
+        ax3.set_title("EI Drill Frequency")
+        heatmap_path = os.path.join(tempfile.gettempdir(), "ei_heatmap.png")
+        plt.tight_layout()
+        plt.savefig(heatmap_path)
+        st.image(heatmap_path)
+    else:
+        ei_counts = pd.DataFrame()
+        st.info("No EI drill data found.")
+
+    # ---------------- RECOMMENDATIONS ----------------
+    st.subheader("🎯 Personalized Recommendations")
+    reco_lines = []
+
+    # Low personality traits
+    if not pers_summary.empty:
+        low_traits = pers_summary[pers_summary['score'] < 3.0]
+        if not low_traits.empty:
+            reco_lines.append("Based on your Big Five traits, you may consider the following:")
+            for trait in low_traits.index:
+                reco_lines.append(f"• Improve {trait} through daily habits and journaling.")
+
+    # Low EI drills
+    if not df_ei.empty:
+        drill_freq = df_ei['drill_type'].value_counts()
+        if drill_freq.min() < 3:
+            reco_lines.append("You're missing out on some EI drills. Try doing more of:")
+            underdone = drill_freq[drill_freq < 3].index.tolist()
+            reco_lines.extend([f"• {d}" for d in underdone])
+
+    if reco_lines:
+        for line in reco_lines:
+            st.markdown(line)
+    else:
+        st.info("You're doing great! No specific recommendations this week.")
+
+    # ---------------- PDF EXPORT ----------------
+    if st.button("📄 Download Weekly Report (PDF)"):
+        pdf_path = os.path.join(tempfile.gettempdir(), "weekly_report.pdf")
+        c = canvas.Canvas(pdf_path, pagesize=A4)
+        width, height = A4
+        margin = 50
+        y = height - margin
+
+        def draw_text_block(title, lines, c, y):
+            c.setFont("Helvetica-Bold", 14)
+            c.drawString(margin, y, title)
+            y -= 20
+            c.setFont("Helvetica", 12)
+            for line in lines:
+                if y < 100:
+                    c.showPage()
+                    y = height - margin
+                    c.setFont("Helvetica", 12)
+                c.drawString(margin + 10, y, line)
+                y -= 15
+            return y - 20
+
+        def draw_image_fullpage(path, title):
+            if path and os.path.exists(path):
+                c.showPage()
+                c.setFont("Helvetica-Bold", 14)
+                c.drawString(margin, height - margin - 20, title)
+                c.drawImage(path, margin, height / 2 - 200, width=5.5 * inch, preserveAspectRatio=True)
+
+        # PAGE 1: Text Summary
+        c.setFont("Helvetica-Bold", 16)
+        c.drawString(margin, y, f"MindMentor Report for {name}")
+        y -= 30
+
+        cog_lines = [f"{task}: Score={row['score']} | RT={row['reaction_time']}s"
+                     for task, row in cog_summary.iterrows()] if not cog_summary.empty else ["No cognitive data available."]
+        pers_lines = [f"{trait}: {row['score']}" for trait, row in pers_summary.iterrows()] if not pers_summary.empty else ["No personality data available."]
+        ei_lines = [f"{row['drill_type']}: {row['Count']} times" for _, row in ei_counts.iterrows()] if not ei_counts.empty else ["No EI drill data available."]
+
+        y = draw_text_block("🧠 Cognitive Task Summary:", cog_lines, c, y)
+        y = draw_text_block("🧬 Personality Trait Summary:", pers_lines, c, y)
+        y = draw_text_block("💖 EI Drill Activity:", ei_lines, c, y)
+
+        # PAGE 2-4: Charts
+        draw_image_fullpage(cog_chart_path, "📊 Cognitive Task Chart")
+        draw_image_fullpage(radar_path, "🧠 Personality Radar Chart")
+        draw_image_fullpage(heatmap_path, "💖 EI Drill Heatmap")
+
+        c.save()
+        with open(pdf_path, "rb") as f:
+            st.download_button("📥 Download PDF", f, file_name="MindMentor_Report.pdf")