Update app.py

app.py

@@ -200,34 +200,133 @@ def plot_historical_data(historical_data):
         plt.axis('off')
         return plt
 
-def create_evolution_chart(data):
-    """Create an evolution chart from data, with error handling"""
+def create_evolution_chart(data, forecast_months=6, growth_scenario="Moderate"):
+    """Create an interactive evolution chart from data using Plotly"""
     try:
-        df = pd.DataFrame(data)
-        
-        plt.figure(figsize=(10, 5))
-        plt.plot(df['month'], df['searchVolume'], marker='o', label='Search Volume')
-        plt.plot(df['month'], df['competitionScore']*20, marker='s', label='Competition Score')
-        plt.plot(df['month'], df['intentClarity']*20, marker='^', label='Intent Clarity')
-        
-        plt.title('Predicted Evolution')
-        plt.xlabel('Month')
-        plt.ylabel('Value')
-        plt.legend()
-        plt.grid(True, linestyle='--', alpha=0.7)
-        plt.tight_layout()
+        import plotly.graph_objects as go
+        from plotly.subplots import make_subplots
+        
+        # Create figure
+        fig = make_subplots(specs=[[{"secondary_y": True}]])
+        
+        # Add traces
+        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>"
+            )
+        )
         
-        return plt
+        fig.add_trace(
+            go.Scatter(
+                x=[item["month"] for item in data],
+                y=[item["competitionScore"] 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
+        )
+        
+        fig.add_trace(
+            go.Scatter(
+                x=[item["month"] for item in data],
+                y=[item["intentClarity"] 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"
+            )
+        )
+        
+        # Customize 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"
+        )
+        
+        # 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)}")
-        # Return a simple error plot
-        plt.figure(figsize=(10, 5))
-        plt.text(0.5, 0.5, f"Error creating chart: {str(e)}", 
-                 horizontalalignment='center', verticalalignment='center')
-        plt.axis('off')
-        return plt
+        # 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)
+        )
+        return fig
 
-def analyze_keyword(keyword, progress=gr.Progress()):
+def analyze_keyword(keyword, forecast_months=6, growth_scenario="Moderate", progress=gr.Progress()):
     """Main function to analyze a keyword"""
     if not keyword or not keyword.strip():
         return (
@@ -365,19 +464,37 @@ def analyze_keyword(keyword, progress=gr.Progress()):
             "Visual search integration"
         ]
         
-        # Evolution chart data (simulated)
-        evolution_data = [
-            {"month": "Jan", "searchVolume": 1000, "competitionScore": 45, "intentClarity": 80},
-            {"month": "Feb", "searchVolume": 1200, "competitionScore": 48, "intentClarity": 82},
-            {"month": "Mar", "searchVolume": 1100, "competitionScore": 52, "intentClarity": 85},
-            {"month": "Apr", "searchVolume": 1400, "competitionScore": 55, "intentClarity": 88},
-            {"month": "May", "searchVolume": 1800, "competitionScore": 58, "intentClarity": 90},
-            {"month": "Jun", "searchVolume": 2200, "competitionScore": 60, "intentClarity": 92}
-        ]
+        # Generate more realistic and keyword-specific evolution data
+        base_volume = 1000 + (len(keyword) * 100)
+        
+        # Adjust growth factor based on scenario
+        if growth_scenario == "Conservative":
+            growth_factor = 1.05 + (0.02 * (sum(ord(c) for c in keyword) % 5))
+        elif growth_scenario == "Aggressive":
+            growth_factor = 1.15 + (0.05 * (sum(ord(c) for c in keyword) % 5))
+        else:  # Moderate
+            growth_factor = 1.1 + (0.03 * (sum(ord(c) for c in keyword) % 5))
+        
+        evolution_data = []
+        months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"][:int(forecast_months)]
+        current_volume = base_volume
+        
+        for month in months:
+            # Add some randomness to make it look more realistic
+            np.random.seed(sum(ord(c) for c in month + keyword))
+            random_factor = 0.9 + (0.2 * np.random.random())
+            current_volume *= growth_factor * random_factor
+            
+            evolution_data.append({
+                "month": month,
+                "searchVolume": int(current_volume),
+                "competitionScore": min(95, 45 + (months.index(month) * 3) + (sum(ord(c) for c in keyword) % 10)),
+                "intentClarity": min(95, 80 + (months.index(month) * 2) + (sum(ord(c) for c in keyword) % 5))
+            })
         
         progress(0.8, desc="Creating visualizations...")
-        # Create plots
-        evolution_chart = create_evolution_chart(evolution_data)
+        # Create interactive evolution chart
+        evolution_chart = create_evolution_chart(evolution_data, forecast_months, growth_scenario)
         
         # Generate HTML for token visualization
         token_viz_html = generate_token_visualization_html(token_analysis, full_token_analysis)
@@ -397,7 +514,12 @@ def analyze_keyword(keyword, progress=gr.Progress()):
             "tokenAnalysis": full_token_analysis,
             "intentAnalysis": intent_analysis,
             "evolutionPotential": evolution_potential,
-            "predictedTrends": trends
+            "predictedTrends": trends,
+            "forecast": {
+                "months": forecast_months,
+                "scenario": growth_scenario,
+                "data": evolution_data
+            }
         }
         
         progress(1.0, desc="Analysis complete!")
@@ -582,7 +704,7 @@ def generate_full_analysis_html(keyword, token_analysis, intent_analysis, evolut
                     <div style="font-size: 12px; margin-bottom: 8px;">
                         <span style="font-weight: 500;">Origin: </span>
                         <span>{token['origin']['era']}, </span>
-                        <span style="font-style: italic;">{token['origin']['language']}</span>
+<span style="font-style: italic;">{token['origin']['language']}</span>
                     </div>
                     <div style="font-size: 12px; margin-bottom: 12px;">{token['origin']['note']}</div>
                     
@@ -623,6 +745,11 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
         with gr.Column():
             input_text = gr.Textbox(label="Enter keyword to analyze", placeholder="e.g. artificial intelligence")
             
+            # Add forecast settings
+            with gr.Accordion("Forecast Settings", open=False):
+                forecast_months = gr.Slider(minimum=3, maximum=12, value=6, step=1, label="Forecast Months")
+                growth_scenario = gr.Radio(["Conservative", "Moderate", "Aggressive"], value="Moderate", label="Growth Scenario")
+            
             # Add loading indicator
             status_html = gr.HTML('<div style="color:gray;text-align:center;">Enter a keyword and click "Analyze DNA"</div>')
             
@@ -642,7 +769,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
                     analysis_html = gr.HTML()
                 
                 with gr.Tab("Evolution Chart"):
-                    evolution_chart = gr.Plot()
+                    evolution_chart = gr.Plot(label="Keyword Evolution Forecast")
                 
                 with gr.Tab("Raw Data"):
                     json_output = gr.JSON()
@@ -653,7 +780,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
         outputs=status_html
     ).then(
         analyze_keyword,
-        inputs=[input_text],
+        inputs=[input_text, forecast_months, growth_scenario],
         outputs=[token_viz_html, analysis_html, json_output, evolution_chart]
     ).then(
         lambda: '<div style="color:green;text-align:center;">Analysis complete!</div>',
@@ -662,8 +789,12 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
     
     # Example buttons
     for btn in example_btns:
+        # Define the function that will be called when an example button is clicked
+        def set_example(btn_label):
+            return btn_label
+        
         btn.click(
-            lambda btn_text: btn_text,
+            set_example,
             inputs=[btn],
             outputs=[input_text]
         ).then(
@@ -671,7 +802,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
             outputs=status_html
         ).then(
             analyze_keyword,
-            inputs=[input_text],
+            inputs=[input_text, forecast_months, growth_scenario],
             outputs=[token_viz_html, analysis_html, json_output, evolution_chart]
         ).then(
             lambda: '<div style="color:green;text-align:center;">Analysis complete!</div>',