Update src/streamlit_app.py

src/streamlit_app.py

@@ -35,13 +35,22 @@ def render_chart_section(title, func, key_id):
         col1, col2 = st.columns([2, 1])
         with col1:
             fig, ax = plt.subplots()
-            func() # Ye plotting function execute karega
+            
+            # Check if function takes an 'ax' argument or not
+            try:
+                # Matches per Season / Top Teams logic
+                func(ax) 
+            except TypeError:
+                # Lambda plots logic (Seaborn/Pie)
+                func() 
+            
             if any(x in title for x in ["Venue", "Season", "City"]):
                 plt.xticks(rotation=45)
-            st.pyplot(fig, use_container_width=True) # Container width se jumping rukegi
+            
+            st.pyplot(fig, use_container_width=True)
+            plt.close(fig)
         with col2:
             ai_explainer_ui(fig, key_id)
-        plt.close(fig)
         
 
 # Now, use this variable throughout your app
@@ -143,7 +152,8 @@ def ai_explainer_ui(fig, key_id):
                 )
                 
                 if response.status_code == 200:
-                    st.session_state[state_key] = response.json()['explanation']
+                    explanation_text = response.json().get('explanation', "No explanation found.")
+                    # st.session_state[state_key] = response.json()['explanation']
 
                     st.session_state[state_key] = explanation_text
                     info_slot.info(explanation_text)
@@ -153,6 +163,28 @@ def ai_explainer_ui(fig, key_id):
                 st.error(f"Connection Error: {e}")
 
 
+def plot_matches_per_season(ax):
+    season_counts = (
+        df2_filtered['season']
+        .value_counts()
+        .sort_index(key=lambda s: s.map(lambda x: int(x) if str(x).isdigit() else x))
+    )
+    ax.plot(season_counts.index.astype(str), season_counts.values,
+            marker='o', color='#1f77b4', linewidth=2)
+    ax.set_xlabel("Season")
+    ax.set_ylabel("Matches Played")
+
+def plot_top_teams(ax):
+    team_runs = (
+        df_filtered.groupby('batting_team')['runs_total']
+        .sum()
+        .sort_values(ascending=False)
+        .head(10)
+    )
+    colors = plt.cm.viridis_r([i/9 for i in range(10)])
+    ax.barh(team_runs.index[::-1], team_runs.values[::-1], color=colors)
+
+
         
 st.title(f"🏏 {analysis_mode}")
 if analysis_mode == "Overview":
@@ -208,11 +240,11 @@ if analysis_mode == "Overview":
         # fig1.tight_layout()          # ← instance method, not plt.tight_layout()
         # st.pyplot(fig1)
 
-        render_chart_section("Matches per Season", fig1, "overview_season")
+        render_chart_section("Matches per Season", plot_matches_per_season, "overview_season")
         
-        plt.close(fig1)              # ← free memory immediately
-        ai_explainer_ui(fig1, "overview_season")
-    st.divider()
+        # plt.close(fig1)              # ← free memory immediately
+        # ai_explainer_ui(fig1, "overview_season")
+        st.divider()
 
     with st.container(border=True):
         st.subheader("Top 10 Run Scoring Teams")
@@ -238,11 +270,11 @@ if analysis_mode == "Overview":
         # fig2.tight_layout()
         # st.pyplot(fig2)
 
-        render_chart_section("Top 10 Run Scoring Teams", fig2, "overview_teams")
-        plt.close(fig2)
-        ai_explainer_ui(fig2, "overview_teams")
+        render_chart_section("Top 10 Run Scoring Teams", plot_top_teams, "overview_teams")
+        # plt.close(fig2)
+        # ai_explainer_ui(fig2, "overview_teams")
 
-    st.divider()
+        st.divider()
 
     # ── Run distribution ─────────────────────────────────────────────────────
     st.subheader("Run Distribution per Ball")
@@ -384,6 +416,11 @@ elif analysis_mode == "Ball-by-Ball Analysis":
     ]
 
     for i, (title, func) in enumerate(bb_plots):
+
+        def plot_dist(ax):
+            run_counts = df_filtered['runs_total'].value_counts().sort_index()
+            ax.bar(run_counts.index.astype(str), run_counts.values, color='pink', alpha=0.8)
+            ax.set_xlabel("Runs per Ball")
         # st.subheader(f"{i+1}. {title}")
         # col1, col2 = st.columns([2, 1])
         # with col1:
@@ -394,5 +431,5 @@ elif analysis_mode == "Ball-by-Ball Analysis":
         # with col2:
         #     ai_explainer_ui(fig, f"bb_btn_{i}")
 
-        render_chart_section(f"{i+1}. {title}", func, f"bb_btn_{i}")
+        render_chart_section("Run Distribution per Ball", plot_dist, "overview_distribution")
         st.divider()