Create app.py

app.py

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+import dash
+from dash import dcc, html, Input, Output, State
+import dash_bootstrap_components as dbc
+from transformers import pipeline
+import plotly.graph_objects as go
+import os
+
+# Initialize Hugging Face pipelines
+try:
+    sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    text_generator = pipeline("text-generation", model="gpt2", max_length=100)
+except Exception as e:
+    print(f"Error loading models: {e}")
+    sentiment_pipeline = None
+    text_generator = None
+
+# Initialize Dash app
+app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+app.title = "Hugging Face Dash Demo"
+
+# Define the layout
+app.layout = dbc.Container([
+    dbc.Row([
+        dbc.Col([
+            html.H1("🤗 Hugging Face + Dash Demo", className="text-center mb-4"),
+            html.Hr(),
+        ])
+    ]),
+    
+    dbc.Row([
+        dbc.Col([
+            dbc.Card([
+                dbc.CardBody([
+                    html.H4("Sentiment Analysis", className="card-title"),
+                    dcc.Textarea(
+                        id='sentiment-input',
+                        placeholder='Enter text to analyze sentiment...',
+                        style={'width': '100%', 'height': 100},
+                        className="mb-3"
+                    ),
+                    dbc.Button("Analyze Sentiment", id="sentiment-btn", color="primary", className="mb-3"),
+                    html.Div(id='sentiment-output')
+                ])
+            ])
+        ], width=6),
+        
+        dbc.Col([
+            dbc.Card([
+                dbc.CardBody([
+                    html.H4("Text Generation", className="card-title"),
+                    dcc.Textarea(
+                        id='generation-input',
+                        placeholder='Enter prompt for text generation...',
+                        style={'width': '100%', 'height': 100},
+                        className="mb-3"
+                    ),
+                    dbc.Button("Generate Text", id="generation-btn", color="success", className="mb-3"),
+                    html.Div(id='generation-output')
+                ])
+            ])
+        ], width=6)
+    ], className="mb-4"),
+    
+    dbc.Row([
+        dbc.Col([
+            dbc.Card([
+                dbc.CardBody([
+                    html.H4("Sentiment Score Visualization", className="card-title"),
+                    dcc.Graph(id='sentiment-graph')
+                ])
+            ])
+        ])
+    ])
+], fluid=True)
+
+# Callback for sentiment analysis
+@app.callback(
+    [Output('sentiment-output', 'children'),
+     Output('sentiment-graph', 'figure')],
+    [Input('sentiment-btn', 'n_clicks')],
+    [State('sentiment-input', 'value')]
+)
+def analyze_sentiment(n_clicks, text):
+    if not n_clicks or not text or not sentiment_pipeline:
+        return "Enter text and click 'Analyze Sentiment'", {}
+    
+    try:
+        result = sentiment_pipeline(text)
+        label = result[0]['label']
+        score = result[0]['score']
+        
+        # Create output
+        output = dbc.Alert([
+            html.H5(f"Sentiment: {label}"),
+            html.P(f"Confidence: {score:.2%}")
+        ], color="info")
+        
+        # Create visualization
+        colors = {'POSITIVE': 'green', 'NEGATIVE': 'red', 'NEUTRAL': 'orange'}
+        fig = go.Figure(data=[
+            go.Bar(x=[label], y=[score], marker_color=colors.get(label, 'blue'))
+        ])
+        fig.update_layout(
+            title="Sentiment Analysis Result",
+            xaxis_title="Sentiment",
+            yaxis_title="Confidence Score",
+            yaxis=dict(range=[0, 1])
+        )
+        
+        return output, fig
+        
+    except Exception as e:
+        return dbc.Alert(f"Error: {str(e)}", color="danger"), {}
+
+# Callback for text generation
+@app.callback(
+    Output('generation-output', 'children'),
+    [Input('generation-btn', 'n_clicks')],
+    [State('generation-input', 'value')]
+)
+def generate_text(n_clicks, prompt):
+    if not n_clicks or not prompt or not text_generator:
+        return "Enter a prompt and click 'Generate Text'"
+    
+    try:
+        result = text_generator(prompt, max_length=len(prompt.split()) + 50, num_return_sequences=1)
+        generated_text = result[0]['generated_text']
+        
+        return dbc.Alert([
+            html.H5("Generated Text:"),
+            html.P(generated_text)
+        ], color="success")
+        
+    except Exception as e:
+        return dbc.Alert(f"Error: {str(e)}", color="danger")
+
+# Run the app
+if __name__ == '__main__':
+    # Hugging Face Spaces requires the app to run on port 7860
+    app.run_server(host='0.0.0.0', port=7860, debug=False)