Update app.py

app.py

@@ -1,54 +1,439 @@
 import streamlit as st
-from pages import (
-    render_home,
-    render_dashboard,
-    render_analytics,
-    render_brainstorm_page,
-    render_chat
-)
-from chatbot import ChatbotManager  # Import the new ChatbotManager
-
-
-def main():
-    st.set_page_config(
-        page_title="Prospira",
-        page_icon="🚀",
-        layout="centered",
-        initial_sidebar_state="expanded"
-    )
-
-    # Initialize the chatbot manager (will load the model)
-    chatbot_manager = ChatbotManager()
-
-    # Sidebar configuration
-    with st.sidebar:
-        st.image("https://via.placeholder.com/150x50?text=Prospira", width=150)
-        st.title("Navigation")
-        
-        # Create a selection box to choose between pages
-        page = st.radio("Select a page", 
-                       ["Home", "Dashboard", "Analytics", "Brainstorm", "Chat"],
-                       label_visibility="collapsed")
-        
-        st.markdown("---")
-        st.caption(f"Model: Blenderbot-400M")
-        st.caption(f"Running on: {'GPU' if torch.cuda.is_available() else 'CPU'}")
-
-    # Page routing
-    if page == "Home":
-        render_home()
-    elif page == "Dashboard":
-        render_dashboard()
-    elif page == "Analytics":
-        render_analytics()
-    elif page == "Brainstorm":
-        render_brainstorm_page()
-    elif page == "Chat":
-        # Pass the chatbot manager to the chat page
-        render_chat(chatbot_manager)
-
-
-if __name__ == "__main__":
-    # Add torch import at runtime if not using it elsewhere
-    import torch
-    main()
+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(
+@@ -450,5 +30,6 @@ def main():
+450	      elif page == "Chat":
+451	          render_chat()
+452	  
+  
+453	  if __name__ == "__main__":
+454	      main()