Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| 2 - import plotly.express as px | |
| 3 - import plotly.graph_objects as go | |
| 4 - import pandas as pd | |
| 5 - import numpy as np | |
| 6 - from datetime import datetime, timedelta | |
| 7 - from typing import Dict, List, Any | |
| 8 - import streamlit as st | |
| 9 - import streamlit.components.v1 as components | |
| 10 - | |
| 11 - # --- Data Processing Class --- | |
| 12 - class DataProcessor: | |
| 13 - def __init__(self): | |
| 14 - self.data = None | |
| 15 - self.numeric_columns = [] | |
| 16 - self.categorical_columns = [] | |
| 17 - | |
| 18 - self.date_columns = [] | |
| 19 - | |
| 20 - def load_data(self, file) -> bool: | |
| 21 - try: | |
| 22 - self.data = pd.read_csv(file) | |
| 23 - self._classify_columns() | |
| 24 - return True | |
| 25 - except Exception as e: | |
| 26 - st.error(f"Error loading data: {str(e)}") | |
| 27 - return False | |
| 28 - | |
| 29 - def _classify_columns(self): | |
| 30 - for col in self.data.columns: | |
| 31 - if pd.api.types.is_numeric_dtype(self.data[col]): | |
| 32 - self.numeric_columns.append(col) | |
| 33 - elif pd.api.types.is_datetime64_any_dtype(self.data[col]): | |
| 34 - self.date_columns.append(col) | |
| 35 - else: | |
| 36 - try: | |
| 37 - pd.to_datetime(self.data[col]) | |
| 38 - self.date_columns.append(col) | |
| 39 - except: | |
| 40 - self.categorical_columns.append(col) | |
| 41 - | |
| 42 - def get_basic_stats(self) -> Dict[str, Any]: | |
| 43 - if self.data is None: | |
| 44 - return {} | |
| 45 - | |
| 46 - stats = { | |
| 47 - 'summary': self.data[self.numeric_columns].describe(), | |
| 48 - 'missing_values': self.data.isnull().sum(), | |
| 49 - 'row_count': len(self.data), | |
| 50 - 'column_count': len(self.data.columns) | |
| 51 - } | |
| 52 - return stats | |
| 53 - | |
| 54 - def create_visualization(self, chart_type: str, x_col: str, y_col: str, color_col: str = None) -> go.Figure: | |
| 55 - if chart_type == "Line Plot": | |
| 56 - fig = px.line(self.data, x=x_col, y=y_col, color=color_col) | |
| 57 - elif chart_type == "Bar Plot": | |
| 58 - fig = px.bar(self.data, x=x_col, y=y_col, color=color_col) | |
| 59 - elif chart_type == "Scatter Plot": | |
| 60 - fig = px.scatter(self.data, x=x_col, y=y_col, color=color_col) | |
| 61 - elif chart_type == "Box Plot": | |
| 62 - fig = px.box(self.data, x=x_col, y=y_col, color=color_col) | |
| 63 - else: | |
| 64 - fig = px.histogram(self.data, x=x_col, color=color_col) | |
| 65 - | |
| 66 - return fig | |
| 67 - | |
| 68 - class BrainstormManager: | |
| 69 - def __init__(self): | |
| 70 - if 'products' not in st.session_state: | |
| 71 - st.session_state.products = {} | |
| 72 - | |
| 73 - def generate_product_form(self) -> Dict: | |
| 74 - with st.form("product_form"): | |
| 75 - basic_info = { | |
| 76 - "name": st.text_input("Product Name"), | |
| 77 - "category": st.selectbox("Category", ["Digital", "Physical", "Service"]), | |
| 78 - "description": st.text_area("Description"), | |
| 79 - "target_audience": st.multiselect("Target Audience", | |
| 80 - ["Students", "Professionals", "Businesses", "Seniors", "Youth"]), | |
| 81 - "price_range": st.slider("Price Range ($)", 0, 1000, (50, 200)), | |
| 82 - "launch_date": st.date_input("Expected Launch Date") | |
| 83 - } | |
| 84 - | |
| 85 - st.subheader("Market Analysis") | |
| 86 - market_analysis = { | |
| 87 - "competitors": st.text_area("Main Competitors (one per line)"), | |
| 88 - "unique_features": st.text_area("Unique Selling Points"), | |
| 89 - "market_size": st.selectbox("Market Size", | |
| 90 - ["Small", "Medium", "Large", "Enterprise"]), | |
| 91 - "growth_potential": st.slider("Growth Potential", 1, 10) | |
| 92 - } | |
| 93 - | |
| 94 - submitted = st.form_submit_button("Save Product") | |
| 95 - return basic_info, market_analysis, submitted | |
| 96 - | |
| 97 - def analyze_product(self, product_data: Dict) -> Dict: | |
| 98 - insights = { | |
| 99 - "market_opportunity": self._calculate_opportunity_score(product_data), | |
| 100 - "suggested_price": self._suggest_price(product_data), | |
| 101 - "risk_factors": self._identify_risks(product_data), | |
| 102 - "next_steps": self._generate_next_steps(product_data) | |
| 103 - } | |
| 104 - return insights | |
| 105 - | |
| 106 - def _calculate_opportunity_score(self, data: Dict) -> int: | |
| 107 - score = 0 | |
| 108 - if data.get("market_size") == "Large": | |
| 109 - score += 3 | |
| 110 - if len(data.get("target_audience", [])) >= 2: | |
| 111 - score += 2 | |
| 112 - if data.get("growth_potential", 0) > 7: | |
| 113 - score += 2 | |
| 114 - return min(score, 10) | |
| 115 - | |
| 116 - def _suggest_price(self, data: Dict) -> float: | |
| 117 - base_price = sum(data.get("price_range", (0, 0))) / 2 | |
| 118 - if data.get("market_size") == "Enterprise": | |
| 119 - base_price *= 1.5 | |
| 120 - return round(base_price, 2) | |
| 121 - | |
| 122 - def _identify_risks(self, data: Dict) -> List[str]: | |
| 123 - risks = [] | |
| 124 - if data.get("competitors"): | |
| 125 - risks.append("Competitive market - differentiation crucial") | |
| 126 - if len(data.get("target_audience", [])) < 2: | |
| 127 - risks.append("Narrow target audience - consider expansion") | |
| 128 - return risks | |
| 129 - | |
| 130 - def _generate_next_steps(self, data: Dict) -> List[str]: | |
| 131 - steps = [ | |
| 132 - "Create detailed product specification", | |
| 133 - "Develop MVP timeline", | |
| 134 - "Plan marketing strategy" | |
| 135 - ] | |
| 136 - if data.get("market_size") == "Enterprise": | |
| 137 - steps.append("Prepare enterprise sales strategy") | |
| 138 - return steps | |
| 139 - | |
| 140 - # --- Sample Data Generation --- | |
| 141 - def generate_sample_data(): | |
| 142 - dates = pd.date_range(start='2024-01-01', end='2024-01-31', freq='D') | |
| 143 - return pd.DataFrame({ | |
| 144 - 'Date': dates, | |
| 145 - 'Revenue': np.random.normal(1000, 100, len(dates)), | |
| 146 - 'Users': np.random.randint(100, 200, len(dates)), | |
| 147 - 'Engagement': np.random.uniform(0.5, 0.9, len(dates)), | |
| 148 - 'Category': np.random.choice(['A', 'B', 'C'], len(dates)) | |
| 149 - }) | |
| 150 - | |
| 151 - # --- Page Rendering Functions --- | |
| 152 - def render_dashboard(): | |
| 153 - st.header("📊 Comprehensive Business Performance Dashboard") | |
| 154 - | |
| 155 - # Generate sample data with more complex structure | |
| 156 - data = generate_sample_data() | |
| 157 - data['Profit_Margin'] = data['Revenue'] * np.random.uniform(0.1, 0.3, len(data)) | |
| 158 - | |
| 159 - # Top-level KPI Section | |
| 160 - col1, col2, col3, col4 = st.columns(4) | |
| 161 - with col1: | |
| 162 - st.metric("Total Revenue", | |
| 163 - f"${data['Revenue'].sum():,.2f}", | |
| 164 - delta=f"{data['Revenue'].pct_change().mean()*100:.2f}%") | |
| 165 - with col2: | |
| 166 - st.metric("Total Users", | |
| 167 - f"{data['Users'].sum():,}", | |
| 168 - delta=f"{data['Users'].pct_change().mean()*100:.2f}%") | |
| 169 - with col3: | |
| 170 - st.metric("Avg Engagement", | |
| 171 - f"{data['Engagement'].mean():.2%}", | |
| 172 - delta=f"{data['Engagement'].pct_change().mean()*100:.2f}%") | |
| 173 - with col4: | |
| 174 - st.metric("Profit Margin", | |
| 175 - f"{data['Profit_Margin'].mean():.2%}", | |
| 176 - delta=f"{data['Profit_Margin'].pct_change().mean()*100:.2f}%") | |
| 177 - | |
| 178 - # Visualization Grid | |
| 179 - col1, col2 = st.columns(2) | |
| 180 - | |
| 181 - with col1: | |
| 182 - st.subheader("Revenue & Profit Trends") | |
| 183 - fig_revenue = go.Figure() | |
| 184 - fig_revenue.add_trace(go.Scatter( | |
| 185 - x=data['Date'], | |
| 186 - y=data['Revenue'], | |
| 187 - mode='lines', | |
| 188 - name='Revenue', | |
| 189 - line=dict(color='blue') | |
| 190 - )) | |
| 191 - fig_revenue.add_trace(go.Scatter( | |
| 192 - x=data['Date'], | |
| 193 - y=data['Profit_Margin'], | |
| 194 - mode='lines', | |
| 195 - name='Profit Margin', | |
| 196 - line=dict(color='green') | |
| 197 - )) | |
| 198 - fig_revenue.update_layout(height=350) | |
| 199 - st.plotly_chart(fig_revenue, use_container_width=True) | |
| 200 - | |
| 201 - with col2: | |
| 202 - st.subheader("User Engagement Analysis") | |
| 203 - fig_engagement = px.scatter( | |
| 204 - data, | |
| 205 - x='Users', | |
| 206 - y='Engagement', | |
| 207 - color='Category', | |
| 208 - size='Revenue', | |
| 209 - hover_data=['Date'], | |
| 210 - title='User Engagement Dynamics' | |
| 211 - ) | |
| 212 - fig_engagement.update_layout(height=350) | |
| 213 - st.plotly_chart(fig_engagement, use_container_width=True) | |
| 214 - | |
| 215 - # Category Performance | |
| 216 - st.subheader("Category Performance Breakdown") | |
| 217 - category_performance = data.groupby('Category').agg({ | |
| 218 - 'Revenue': 'sum', | |
| 219 - 'Users': 'sum', | |
| 220 - 'Engagement': 'mean' | |
| 221 - }).reset_index() | |
| 222 - | |
| 223 - fig_category = px.bar( | |
| 224 - category_performance, | |
| 225 - x='Category', | |
| 226 - y='Revenue', | |
| 227 - color='Engagement', | |
| 228 - title='Revenue by Category with Engagement Overlay' | |
| 229 - ) | |
| 230 - st.plotly_chart(fig_category, use_container_width=True) | |
| 231 - | |
| 232 - # Bottom Summary | |
| 233 - st.subheader("Quick Insights") | |
| 234 - insights_col1, insights_col2 = st.columns(2) | |
| 235 - | |
| 236 - with insights_col1: | |
| 237 - st.metric("Top Performing Category", | |
| 238 - category_performance.loc[category_performance['Revenue'].idxmax(), 'Category']) | |
| 239 - | |
| 240 - with insights_col2: | |
| 241 - st.metric("Highest Engagement Category", | |
| 242 - category_performance.loc[category_performance['Engagement'].idxmax(), 'Category']) | |
| 243 - | |
| 244 - def render_analytics(): | |
| 245 - st.header("🔍 Data Analytics") | |
| 246 - | |
| 247 - processor = DataProcessor() | |
| 248 - uploaded_file = st.file_uploader("Upload your CSV data", type=['csv']) | |
| 249 - | |
| 250 - if uploaded_file is not None: | |
| 251 - if processor.load_data(uploaded_file): | |
| 252 - st.success("Data loaded successfully!") | |
| 253 - | |
| 254 - tabs = st.tabs(["Data Preview", "Statistics", "Visualization", "Metrics"]) | |
| 255 - | |
| 256 - with tabs[0]: | |
| 257 - st.subheader("Data Preview") | |
| 258 - st.dataframe(processor.data.head()) | |
| 259 - st.info(f"Total rows: {len(processor.data)}, Total columns: {len(processor.data.columns)}") | |
| 260 - | |
| 261 - with tabs[1]: | |
| 262 - st.subheader("Basic Statistics") | |
| 263 - stats = processor.get_basic_stats() | |
| 264 - st.write(stats['summary']) | |
| 265 - | |
| 266 - st.subheader("Missing Values") | |
| 267 - st.write(stats['missing_values']) | |
| 268 - | |
| 269 - with tabs[2]: | |
| 270 - st.subheader("Create Visualization") | |
| 271 - col1, col2, col3 = st.columns(3) | |
| 272 - | |
| 273 - with col1: | |
| 274 - chart_type = st.selectbox( | |
| 275 - "Select Chart Type", | |
| 276 - ["Line Plot", "Bar Plot", "Scatter Plot", "Box Plot", "Histogram"] | |
| 277 - ) | |
| 278 - | |
| 279 - with col2: | |
| 280 - x_col = st.selectbox("Select X-axis", processor.data.columns) | |
| 281 - | |
| 282 - with col3: | |
| 283 - y_col = st.selectbox("Select Y-axis", processor.numeric_columns) if chart_type != "Histogram" else None | |
| 284 - | |
| 285 - color_col = st.selectbox("Select Color Variable (optional)", | |
| 286 - ['None'] + processor.categorical_columns) | |
| 287 - color_col = None if color_col == 'None' else color_col | |
| 288 - | |
| 289 - fig = processor.create_visualization( | |
| 290 - chart_type, | |
| 291 - x_col, | |
| 292 - y_col if y_col else x_col, | |
| 293 - color_col | |
| 294 - ) | |
| 295 - st.plotly_chart(fig, use_container_width=True) | |
| 296 - | |
| 297 - with tabs[3]: | |
| 298 - st.subheader("Column Metrics") | |
| 299 - selected_col = st.selectbox("Select column", processor.numeric_columns) | |
| 300 - | |
| 301 - metrics = { | |
| 302 - 'Mean': processor.data[selected_col].mean(), | |
| 303 - 'Median': processor.data[selected_col].median(), | |
| 304 - 'Std Dev': processor.data[selected_col].std(), | |
| 305 - 'Min': processor.data[selected_col].min(), | |
| 306 - 'Max': processor.data[selected_col].max() | |
| 307 - } | |
| 308 - | |
| 309 - cols = st.columns(len(metrics)) | |
| 310 - for col, (metric, value) in zip(cols, metrics.items()): | |
| 311 - col.metric(metric, f"{value:.2f}") | |
| 312 - | |
| 313 - def render_brainstorm_page(): | |
| 314 - st.title("Product Brainstorm Hub") | |
| 315 - manager = BrainstormManager() | |
| 316 - | |
| 317 - action = st.sidebar.radio("Action", ["View Products", "Create New Product"]) | |
| 318 - | |
| 319 - if action == "Create New Product": | |
| 320 - basic_info, market_analysis, submitted = manager.generate_product_form() | |
| 321 - | |
| 322 - if submitted: | |
| 323 - product_data = {**basic_info, **market_analysis} | |
| 324 - insights = manager.analyze_product(product_data) | |
| 325 - | |
| 326 - product_id = f"prod_{len(st.session_state.products)}" | |
| 327 - st.session_state.products[product_id] = { | |
| 328 - "data": product_data, | |
| 329 - "insights": insights, | |
| 330 - "created_at": str(datetime.now()) | |
| 331 - } | |
| 332 - | |
| 333 - st.success("Product added! View insights in the Products tab.") | |
| 334 - | |
| 335 - else: | |
| 336 - if st.session_state.products: | |
| 337 - for prod_id, product in st.session_state.products.items(): | |
| 338 - with st.expander(f"🎯 {product['data']['name']}"): | |
| 339 - col1, col2 = st.columns(2) | |
| 340 - | |
| 341 - with col1: | |
| 342 - st.subheader("Product Details") | |
| 343 - st.write(f"Category: {product['data']['category']}") | |
| 344 - st.write(f"Target: {', '.join(product['data']['target_audience'])}") | |
| 345 - st.write(f"Description: {product['data']['description']}") | |
| 346 - | |
| 347 - with col2: | |
| 348 - st.subheader("Insights") | |
| 349 - st.metric("Opportunity Score", f"{product['insights']['market_opportunity']}/10") | |
| 350 - st.metric("Suggested Price", f"${product['insights']['suggested_price']}") | |
| 351 - | |
| 352 - st.write("**Risk Factors:**") | |
| 353 - for risk in product['insights']['risk_factors']: | |
| 354 - st.write(f"- {risk}") | |
| 355 - | |
| 356 - st.write("**Next Steps:**") | |
| 357 - for step in product['insights']['next_steps']: | |
| 358 - st.write(f"- {step}") | |
| 359 - else: | |
| 360 - st.info("No products yet. Create one to get started!") | |
| 361 - | |
| 362 - | |
| 363 - | |
| 364 | |
| 365 - def generate_response(self, prompt: str, context: list = None) -> str: | |
| 366 - if not self.model or not self.tokenizer: | |
| 367 - return "LLM not initialized. Please check model configuration." | |
| 368 - | |
| 369 - # Prepare conversation context | |
| 370 - if context is None: | |
| 371 - context = [] | |
| 372 - | |
| 373 - # Create full prompt with conversation history | |
| 374 - full_prompt = "".join([f"{msg['role']}: {msg['content']}\n" for msg in context]) | |
| 375 - full_prompt += f"user: {prompt}\nassistant: " | |
| 376 - | |
| 377 - # Tokenize input | |
| 378 - input_ids = self.tokenizer(full_prompt, return_tensors="pt").input_ids.to(self.model.device) | |
| 379 - | |
| 380 - # Generate response | |
| 381 - try: | |
| 382 - output = self.model.generate( | |
| 383 - input_ids, | |
| 384 - max_length=500, | |
| 385 - num_return_sequences=1, | |
| 386 - no_repeat_ngram_size=2, | |
| 387 - temperature=0.7, | |
| 388 - top_p=0.9 | |
| 389 - ) | |
| 390 - | |
| 391 - # Decode response | |
| 392 - response = self.tokenizer.decode(output[0], skip_special_tokens=True) | |
| 393 - | |
| 394 - # Extract only the new part of the response | |
| 395 - response = response[len(full_prompt):].strip() | |
| 396 - | |
| 397 - return response | |
| 398 - except Exception as e: | |
| 399 - return f"Response generation error: {e}" | |
| 400 - | |
| 401 - def render_chat(): | |
| 402 - st.header("💬AI Business Mentor") | |
| 403 - st.title("🤖 Prospira AI Business Mentor") | |
| 404 - | |
| 405 - iframe_code = """ | |
| 406 - <iframe | |
| 407 - src="https://demoorganisation34-vinay.hf.space" | |
| 408 - frameborder="0" | |
| 409 - width="850" | |
| 410 - height="450" | |
| 411 - ></iframe> | |
| 412 - | |
| 413 - | |
| 414 - """ | |
| 415 - components.html(iframe_code, height=600) | |
| 416 - | |
| 417 - def render_home(): | |
| 418 - st.title("🚀 Welcome to Prospira") | |
| 419 - st.subheader("📊 Data-Driven Solutions for Businesses and Creators") | |
| 420 - st.markdown(""" | |
| 421 - **Prospira** empowers businesses and creators to enhance their content, products, and marketing strategies using AI-driven insights. | |
| 422 - | |
| 423 - ### **✨ Key Features** | |
| 424 - - **📈 Performance Analytics:** Real-time insights into business metrics. | |
| 425 - - **🔎 Competitive Analysis:** Benchmark your business against competitors. | |
| 426 - - **💡 Smart Product Ideas:** AI-generated recommendations for future products and content. | |
| 427 - - **🧠 AI Business Mentor:** Personalized AI guidance for strategy and growth. | |
| 428 - Explore how **Prospira** can help optimize your decision-making and drive success! 💡🚀 | |
| 429 - """) | |
| 430 | |
| 431 def main(): | |
| 432 st.set_page_config( | |
| 450 elif page == "Chat": | |
| 451 render_chat() | |
| 452 | |
| 453 if __name__ == "__main__": | |
| 454 main() |