Update app.py

app.py

@@ -201,129 +201,66 @@ def plot_historical_data(historical_data):
         return plt
 
 def create_evolution_chart(data, forecast_months=6, growth_scenario="Moderate"):
-    """Create an interactive evolution chart from data using Plotly"""
+    """Create a simpler chart that's more compatible with Gradio"""
     try:
         import plotly.graph_objects as go
-        from plotly.subplots import make_subplots
         
-        # Create figure
-        fig = make_subplots(specs=[[{"secondary_y": True}]])
+        # Create a basic figure without subplots
+        fig = go.Figure()
         
-        # Add traces
+        # Add main trace for search volume
         fig.add_trace(
             go.Scatter(
                 x=[item["month"] for item in data],
                 y=[item["searchVolume"] for item in data],
                 name="Search Volume",
                 line=dict(color="#8884d8", width=3),
-                hovertemplate="Month: %{x}<br>Volume: %{y}<extra></extra>"
+                mode="lines+markers"
             )
         )
         
+        # Scale the other metrics to be visible on the same chart
+        max_volume = max([item["searchVolume"] for item in data])
+        scale_factor = max_volume / 100
+        
+        # Add competition score (scaled)
         fig.add_trace(
             go.Scatter(
                 x=[item["month"] for item in data],
-                y=[item["competitionScore"] for item in data],
+                y=[item["competitionScore"] * scale_factor for item in data],
                 name="Competition Score",
-                line=dict(color="#82ca9d", width=3, dash="dot"),
-                hovertemplate="Month: %{x}<br>Score: %{y}<extra></extra>"
-            ),
-            secondary_y=True
+                line=dict(color="#82ca9d", width=2, dash="dot"),
+                mode="lines+markers"
+            )
         )
         
+        # Add intent clarity (scaled)
         fig.add_trace(
             go.Scatter(
                 x=[item["month"] for item in data],
-                y=[item["intentClarity"] for item in data],
+                y=[item["intentClarity"] * scale_factor for item in data],
                 name="Intent Clarity",
-                line=dict(color="#ffc658", width=3, dash="dash"),
-                hovertemplate="Month: %{x}<br>Clarity: %{y}<extra></extra>"
-            ),
-            secondary_y=True
-        )
-        
-        # Add trend line
-        x_values = list(range(len(data)))
-        y_values = [item["searchVolume"] for item in data]
-        
-        # Simple linear regression
-        slope, intercept = np.polyfit(x_values, y_values, 1)
-        trend_y = [slope * x + intercept for x in x_values]
-        
-        fig.add_trace(
-            go.Scatter(
-                x=[item["month"] for item in data],
-                y=trend_y,
-                name="Trend",
-                line=dict(color="rgba(255, 0, 0, 0.5)", width=2, dash="dot"),
-                hoverinfo="skip"
+                line=dict(color="#ffc658", width=2, dash="dash"),
+                mode="lines+markers"
             )
         )
         
-        # Customize layout
+        # Simple layout
         fig.update_layout(
             title=f"Keyword Evolution Forecast ({growth_scenario} Growth)",
-            title_font=dict(size=20),
-            hovermode="x unified",
-            xaxis=dict(
-                title="Month",
-                titlefont=dict(size=14),
-                showgrid=True,
-                gridcolor="rgba(0,0,0,0.1)"
-            ),
-            yaxis=dict(
-                title="Search Volume",
-                titlefont=dict(size=14),
-                showgrid=True,
-                gridcolor="rgba(0,0,0,0.1)"
-            ),
-            yaxis2=dict(
-                title="Score (0-100)",
-                titlefont=dict(size=14),
-                range=[0, 100]
-            ),
-            legend=dict(
-                orientation="h",
-                yanchor="bottom",
-                y=1.02,
-                xanchor="center",
-                x=0.5
-            ),
-            margin=dict(l=10, r=10, t=80, b=10),
-            height=500,
-            template="plotly_white"
+            xaxis_title="Month",
+            yaxis_title="Value",
+            legend=dict(orientation="h", y=1.1),
+            height=500
         )
         
-        # Add annotations for key insights
-        max_month_index = y_values.index(max(y_values))
-        fig.add_annotation(
-            x=data[max_month_index]["month"],
-            y=max(y_values),
-            text="Peak Volume",
-            showarrow=True,
-            arrowhead=1,
-            ax=0,
-            ay=-40
-        )
-        
-        # Return the figure
         return fig
-    
+        
     except Exception as e:
-        print(f"Error in create_evolution_chart: {str(e)}")
-        # Create a simple error message plot with Plotly
-        import plotly.graph_objects as go
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text=f"Error creating chart: {str(e)}",
-            showarrow=False,
-            font=dict(size=14, color="red")
-        )
-        fig.update_layout(
-            xaxis=dict(showticklabels=False),
-            yaxis=dict(showticklabels=False)
-        )
+        print(f"Error in chart creation: {str(e)}")
+        # Fallback to an even simpler chart
+        fig = go.Figure(data=go.Scatter(x=[1, 2, 3], y=[4, 1, 2]))
+        fig.update_layout(title="Fallback Chart (Error occurred)")
         return fig
 
 def analyze_keyword(keyword, forecast_months=6, growth_scenario="Moderate", progress=gr.Progress()):