', unsafe_allow_html=True)
st.markdown("### Intelligent Software Procurement & Negotiation Platform")
col1, col2, col3 = st.columns(3)
with col1:
st.info("**π― AI Matching**\n\nIntelligent software recommendations based on your needs")
with col2:
st.info("**π Smart Comparison**\n\nMulti-dimensional analysis of features, pricing & TCO")
with col3:
st.info("**π€ Negotiation AI**\n\nGet the best deals with data-driven negotiation strategies")
st.markdown("---")
st.markdown("## π Quick Start")
col1, col2 = st.columns(2)
with col1:
st.markdown("### 1οΈβ£ Tell Us Your Needs")
company_size = st.selectbox("Company Size", ["1-10", "11-50", "51-200", "201-1000", "1000+"])
industry = st.selectbox("Industry", ["Technology", "Healthcare", "Finance", "Education", "Retail", "Other"])
budget = st.selectbox("Monthly Budget", ["<$1K", "$1K-$5K", "$5K-$20K", "$20K-$50K", "$50K+"])
if st.button("π― Get AI Recommendations", type="primary"):
with st.spinner("Analyzing your requirements..."):
prompt = f"""
You are an expert enterprise software consultant. Based on the following company profile, recommend the top 5 software tools or vendor bundles that best fit their needs.
Company Profile:
- Company Size: {company_size} employees
- Industry: {industry}
- Monthly Budget: {budget}
Available software database:
{json.dumps([
{"name": s["name"], "category": s["category"], "pricing": f"${s['pricing_min']}-{s['pricing_max']}/{s['pricing_unit']}"}
for s in SOFTWARE_DATABASE
], indent=2)}
Please perform a holistic evaluation, considering:
1. **Functional Coverage Efficiency** β Prefer software that covers multiple business needs (reduce overlap).
2. **Vendor Consolidation** β Recommend single-vendor bundles when one company provides multiple complementary tools.
3. **Cost Efficiency** β Stay within the monthly budget and note potential savings from reduced redundancy.
4. **Integration Simplicity** β Fewer vendors β lower integration and training overhead.
5. **Scalability and Fit** β Match features and complexity to company size and industry-specific workflows.
Note that the overlap issue should be taken into account. It is necessary to consider the situation where many functions can be accomplished by purchasing lisence from just one company
Provide recommendations with reasoning for each.
"""
response = call_openai(prompt)
st.success("β Recommendations Generated!")
st.markdown(response)
with col2:
st.markdown("### 2οΈβ£ Browse Software Catalog")
categories = ["All"] + list(set([s["category"] for s in SOFTWARE_DATABASE]))
selected_category = st.selectbox("Category", categories)
filtered_software = SOFTWARE_DATABASE if selected_category == "All" else [s for s in SOFTWARE_DATABASE if s["category"] == selected_category]
st.markdown(f"**{len(filtered_software)} software found**")
for software in filtered_software[:5]:
with st.expander(f"**{software['name']}** - {software['category']} β {software['rating']}"):
st.markdown(f"*{software['description']}*")
st.markdown(f"**π° Pricing:** ${software['pricing_min']}-${software['pricing_max']}/{software['pricing_unit']}")
st.markdown(f"**π₯ Users:** {software['users']}")
if st.button(f"Add to Compare", key=f"home_compare_{software['name']}"):
if software not in st.session_state.compare_list:
st.session_state.compare_list.append(software)
st.success(f"Added {software['name']} to comparison!")
else:
st.warning("Already in comparison list")
elif page == "π AI Matching Engine":
st.markdown('
π― AI Software Matching Engine
', unsafe_allow_html=True)
st.markdown("### Let AI help you find the perfect software for your needs")
tab1, tab2 = st.tabs(["π¬ Conversational Analysis", "π Questionnaire"])
with tab1:
st.markdown("#### Chat with our AI to discover your perfect software match")
# Chat interface
for msg in st.session_state.chat_history:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
user_input = st.chat_input("Describe your software needs...")
if user_input:
st.session_state.chat_history.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(user_input)
with st.chat_message("assistant"):
with st.spinner("Analyzing..."):
system_prompt = """You are an expert software procurement consultant. Help users find the best software solutions.
Ask clarifying questions about:
- Company size and structure
- Industry and use cases
- Budget constraints
- Current software stack
- Integration requirements
- Compliance needs
Be conversational and helpful. After gathering enough information, recommend specific software from the database."""
context = f"""
Chat history: {json.dumps(st.session_state.chat_history[-5:])}
Available software: {json.dumps([{"name": s["name"], "category": s["category"], "features": s["features"][:3]} for s in SOFTWARE_DATABASE], indent=2)}
User message: {user_input}
"""
response = call_openai(context, system_prompt)
st.markdown(response)
st.session_state.chat_history.append({"role": "assistant", "content": response})
with tab2:
st.markdown("#### Complete this questionnaire for precise recommendations")
with st.form("requirements_form"):
col1, col2 = st.columns(2)
with col1:
team_size = st.number_input("Team Size", min_value=1, value=10)
industry = st.selectbox("Industry", ["Technology", "Healthcare", "Finance", "Education", "Retail", "Manufacturing", "Other"])
remote_work = st.selectbox("Work Model", ["Fully Remote", "Hybrid", "In-Office"])
budget_range = st.selectbox("Monthly Budget per User", ["<$10", "$10-$30", "$30-$50", "$50-$100", "$100+"])
with col2:
needs = st.multiselect("Primary Needs", [
"Team Communication", "Video Conferencing", "Project Management",
"File Storage", "CRM", "Email", "Design Tools", "Developer Tools",
"Knowledge Management", "Time Tracking"
])
integrations = st.multiselect("Must Integrate With", [
"Slack", "Microsoft Teams", "Google Workspace", "Salesforce",
"Jira", "GitHub", "Zoom"
])
compliance = st.multiselect("Compliance Requirements", [
"GDPR", "HIPAA", "SOC2", "ISO27001"
])
submitted = st.form_submit_button("π― Get AI Recommendations", type="primary")
if submitted:
with st.spinner("Analyzing your requirements with AI..."):
prompt = f"""Analyze these requirements and recommend the best software solutions:
Company Profile:
- Team Size: {team_size} people
- Industry: {industry}
- Work Model: {remote_work}
- Budget per User: {budget_range}
Requirements:
- Primary Needs: {', '.join(needs)}
- Required Integrations: {', '.join(integrations)}
- Compliance: {', '.join(compliance)}
Available software database:
{json.dumps(SOFTWARE_DATABASE, indent=2)}
Provide:
1. Top 5 recommended software with match scores
2. Functional gap analysis
3. Estimated total cost
4. Integration compatibility
5. Compliance coverage
"""
response = call_openai(prompt)
st.success("β Analysis Complete!")
st.markdown("### π― AI Recommendations")
st.markdown(response)
# Extract recommended software
st.markdown("---")
st.markdown("### π Quick Compare Recommended Software")
cols = st.columns(3)
for idx, software in enumerate(SOFTWARE_DATABASE[:3]):
with cols[idx]:
st.markdown(f"**{software['name']}**")
st.markdown(f"β {software['rating']}")
st.markdown(f"π° ${software['pricing_min']}-${software['pricing_max']}")
if st.button(f"Add to Compare", key=f"rec_{software['name']}"):
if software not in st.session_state.compare_list:
st.session_state.compare_list.append(software)
st.success(f"Added!")
elif page == "π Compare Software":
st.markdown('
π Multi-Dimensional Comparison
', unsafe_allow_html=True)
col1, col2 = st.columns([3, 1])
with col1:
st.markdown(f"### Compare up to 4 software solutions")
with col2:
if st.button("ποΈ Clear All"):
st.session_state.compare_list = []
st.rerun()
# Software selector
st.markdown("#### Add Software to Compare")
col1, col2, col3 = st.columns(3)
with col1:
selected_software = st.selectbox(
"Select Software",
[s["name"] for s in SOFTWARE_DATABASE if s not in st.session_state.compare_list],
key="software_selector"
)
with col2:
if st.button("β Add to Comparison", type="primary"):
software = next(s for s in SOFTWARE_DATABASE if s["name"] == selected_software)
if len(st.session_state.compare_list) < 4:
st.session_state.compare_list.append(software)
st.success(f"Added {selected_software}!")
st.rerun()
else:
st.error("Maximum 4 software can be compared")
if len(st.session_state.compare_list) == 0:
st.info("π Add software to start comparing")
else:
st.markdown(f"**{len(st.session_state.compare_list)} software selected**")
# Display comparison cards
cols = st.columns(len(st.session_state.compare_list))
for idx, software in enumerate(st.session_state.compare_list):
with cols[idx]:
st.markdown(f"""
{software['name']}
{software['category']}
""", unsafe_allow_html=True)
st.markdown(f"β **Rating:** {software['rating']}/5.0")
if st.button("Remove", key=f"remove_{idx}"):
st.session_state.compare_list.pop(idx)
st.rerun()
st.markdown("---")
# Comparison tabs
tab1, tab2, tab3, tab4, tab5 = st.tabs([
"π° Pricing", "β¨ Features", "π Integrations", "π TCO Analysis", "π€ AI Insights"
])
with tab1:
st.markdown("### π° Pricing Structure Comparison")
# Pricing comparison table
pricing_data = []
for software in st.session_state.compare_list:
pricing_data.append({
"Software": software["name"],
"Min Price": f"${software['pricing_min']}",
"Max Price": f"${software['pricing_max']}",
"Unit": software["pricing_unit"],
"Negotiable": "β " if software["negotiable"] else "β"
})
df_pricing = pd.DataFrame(pricing_data)
st.dataframe(df_pricing, use_container_width=True)
# Pricing chart
st.markdown("#### Price Range Comparison")
fig = go.Figure()
for software in st.session_state.compare_list:
fig.add_trace(go.Bar(
name=software["name"],
x=["Min Price", "Max Price"],
y=[software["pricing_min"], software["pricing_max"]],
))
fig.update_layout(barmode='group', height=400)
st.plotly_chart(fig, use_container_width=True)
# TCO Calculator
st.markdown("#### π΅ Total Cost of Ownership Calculator")
num_users = st.slider("Number of Users", 1, 500, 50)
contract_length = st.selectbox("Contract Length", ["Monthly", "Annual", "Multi-year"])
st.markdown("**Estimated Annual Cost:**")
for software in st.session_state.compare_list:
avg_price = (software["pricing_min"] + software["pricing_max"]) / 2
annual_cost = avg_price * num_users * 12
discount = 0.15 if software["negotiable"] else 0
final_cost = annual_cost * (1 - discount)
st.metric(
software["name"],
f"${final_cost:,.0f}/year",
f"-${annual_cost * discount:,.0f} (negotiable)" if discount > 0 else "Fixed pricing"
)
with tab2:
st.markdown("### β¨ Feature Matrix Comparison")
all_features = set()
for software in st.session_state.compare_list:
all_features.update(software["features"])
feature_matrix = []
for feature in sorted(all_features):
row = {"Feature": feature}
for software in st.session_state.compare_list:
row[software["name"]] = "β " if feature in software["features"] else "β"
feature_matrix.append(row)
df_features = pd.DataFrame(feature_matrix)
st.dataframe(df_features, use_container_width=True, height=400)
# Feature coverage chart
st.markdown("#### Feature Coverage Score")
coverage_data = []
for software in st.session_state.compare_list:
coverage = (len(software["features"]) / len(all_features)) * 100
coverage_data.append({"Software": software["name"], "Coverage": coverage})
df_coverage = pd.DataFrame(coverage_data)
fig = px.bar(df_coverage, x="Software", y="Coverage",
title="Feature Coverage (%)",
color="Coverage",
color_continuous_scale="Blues")
st.plotly_chart(fig, use_container_width=True)
with tab3:
st.markdown("### π Integration Compatibility")
all_integrations = set()
for software in st.session_state.compare_list:
all_integrations.update(software["integrations"])
integration_matrix = []
for integration in sorted(all_integrations):
row = {"Integration": integration}
for software in st.session_state.compare_list:
row[software["name"]] = "β " if integration in software["integrations"] else "β"
integration_matrix.append(row)
df_integrations = pd.DataFrame(integration_matrix)
st.dataframe(df_integrations, use_container_width=True)
# Compliance comparison
st.markdown("#### π‘οΈ Compliance & Security")
compliance_matrix = []
all_compliance = set()
for software in st.session_state.compare_list:
all_compliance.update(software["compliance"])
for comp in sorted(all_compliance):
row = {"Certification": comp}
for software in st.session_state.compare_list:
row[software["name"]] = "β " if comp in software["compliance"] else "β"
compliance_matrix.append(row)
df_compliance = pd.DataFrame(compliance_matrix)
st.dataframe(df_compliance, use_container_width=True)
with tab4:
st.markdown("### π Total Cost of Ownership (TCO) Analysis")
st.markdown("#### Configure Your Scenario")
col1, col2, col3 = st.columns(3)
with col1:
num_users_tco = st.number_input("Number of Users", 1, 1000, 50, key="tco_users")
with col2:
years = st.selectbox("Time Period", [1, 2, 3, 5], key="tco_years")
with col3:
include_costs = st.multiselect("Include", ["Training", "Migration", "Support"], default=["Training"])
tco_data = []
for software in st.session_state.compare_list:
avg_price = (software["pricing_min"] + software["pricing_max"]) / 2
subscription_cost = avg_price * num_users_tco * 12 * years
training_cost = 100 * num_users_tco if "Training" in include_costs else 0
migration_cost = 5000 if "Migration" in include_costs else 0
support_cost = subscription_cost * 0.1 * years if "Support" in include_costs else 0
total_tco = subscription_cost + training_cost + migration_cost + support_cost
tco_data.append({
"Software": software["name"],
"Subscription": subscription_cost,
"Training": training_cost,
"Migration": migration_cost,
"Support": support_cost,
"Total TCO": total_tco
})
df_tco = pd.DataFrame(tco_data)
# TCO breakdown chart
fig = go.Figure()
for cost_type in ["Subscription", "Training", "Migration", "Support"]:
fig.add_trace(go.Bar(
name=cost_type,
x=df_tco["Software"],
y=df_tco[cost_type]
))
fig.update_layout(barmode='stack', title="TCO Breakdown", height=400)
st.plotly_chart(fig, use_container_width=True)
# TCO table
st.markdown("#### Detailed TCO Breakdown")
st.dataframe(df_tco.style.format({
"Subscription": "${:,.0f}",
"Training": "${:,.0f}",
"Migration": "${:,.0f}",
"Support": "${:,.0f}",
"Total TCO": "${:,.0f}"
}), use_container_width=True)
with tab5:
st.markdown("### π€ AI-Powered Insights")
if st.button("π§ Generate AI Analysis", type="primary"):
with st.spinner("AI is analyzing your comparison..."):
prompt = f"""Analyze this software comparison and provide insights:
Software being compared:
{json.dumps(st.session_state.compare_list, indent=2)}
Provide:
1. **Best Overall Value**: Which offers the best balance of features and price?
2. **Best for Specific Use Cases**: Recommend which software for different scenarios
3. **Cost Optimization**: How to reduce costs while maintaining functionality
4. **Integration Strategy**: Which combination works best together
5. **Risk Assessment**: Potential issues or limitations
6. **Negotiation Opportunities**: Which vendors are most likely to offer discounts
Be specific and actionable."""
response = call_openai(prompt)
st.markdown(response)
st.markdown("---")
st.markdown("#### π Quick Recommendation Matrix")
cols = st.columns(len(st.session_state.compare_list))
for idx, software in enumerate(st.session_state.compare_list):
with cols[idx]:
st.markdown(f"**{software['name']}**")
# Calculate scores
price_score = 5.0 - (software["pricing_max"] / 50) # Simple price score
feature_score = min(5.0, len(software["features"]) / 2)
integration_score = min(5.0, len(software["integrations"]))
st.metric("Price Score", f"{max(1, price_score):.1f}/5")
st.metric("Feature Score", f"{feature_score:.1f}/5")
st.metric("Integration", f"{integration_score:.1f}/5")
elif page == "π‘ Strategy Optimizer":
st.markdown('