Create app.py

app.py

@@ -0,0 +1,195 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from io import StringIO
+from huggingface_hub import InferenceClient
+
+# --------------------------
+# Streamlit Setup
+# --------------------------
+st.set_page_config(
+    page_title="Team Career Progression Assistant",
+    layout="wide"
+)
+
+st.title("🧭 Team Career Progression Assistant")
+st.caption("Program Managers • Scrum Masters • People Leads")
+
+# --------------------------
+# Load LLM client (optional)
+# --------------------------
+@st.cache_resource
+def load_llm():
+    return InferenceClient("google/flan-t5-small")
+
+def generate_llm_plan(row):
+    client = load_llm()
+
+    prompt = f"""
+    You are an expert career coach. Summarize strengths and gaps.
+    Then give a 30-60-90 day plan.
+
+    Profile:
+    Name: {row['Name']}
+    Current Role: {row['CurrentRole']}
+    Years Exp: {row['YearsExperience']}
+    Tech Skills: {row['TechSkillRating']}
+    Soft Skills: {row['SoftSkillRating']}
+    Performance: {row['PerformanceRating']}
+    Leadership: {row['LeadershipInterest']}
+    Domain: {row['DomainInterest']}
+    Career Goal: {row['CareerGoal']}
+    """
+
+    try:
+        response = client.text_generation(prompt, max_new_tokens=200)
+        return response.strip()
+    except Exception as e:
+        return f"LLM Error: {e}"
+
+# --------------------------
+# Compute readiness score
+# --------------------------
+def score(row):
+    years = min(row["YearsExperience"], 10) / 10 * 5
+    tech = row["TechSkillRating"]
+    soft = row["SoftSkillRating"]
+    perf = row["PerformanceRating"]
+    leadership = 5 if str(row["LeadershipInterest"]).lower() == "yes" else 2
+
+    score_5 = (0.3 * years + 0.2 * tech + 0.2 * soft +
+               0.2 * perf + 0.1 * leadership)
+    return round(score_5 / 5 * 100, 1)
+
+# --------------------------
+# Next role + actions
+# --------------------------
+def suggest_next_role(row):
+    s = row["ReadinessScore"]
+
+    if s >= 80:
+        return "Team Lead / Scrum Master"
+    elif s >= 60:
+        return f"Mid-level {row['CurrentRole']}"
+    else:
+        return "Upskill Current Role"
+
+def suggest_actions(row):
+    actions = []
+
+    if row["ReadinessScore"] >= 80:
+        actions = [
+            "Lead small initiatives",
+            "Mentor junior teammates",
+            "Improve decision-making"
+        ]
+    elif row["ReadinessScore"] >= 60:
+        actions = [
+            "Improve core technical skills",
+            "Own a module or feature",
+            "Drive small improvements"
+        ]
+    else:
+        actions = [
+            "Focus on consistency",
+            "Work with mentor weekly",
+            "Upskill using certifications"
+        ]
+
+    return "• " + "\n• ".join(actions)
+
+# --------------------------
+# Sidebar Inputs
+# --------------------------
+with st.sidebar:
+    st.header("📂 Upload CSV or Use Sample")
+    file = st.file_uploader("Upload CSV", type=["csv"])
+    use_sample = st.checkbox("Use sample data", value=True)
+
+    st.header("🤖 LLM Options")
+    use_llm = st.checkbox("Enable AI-generated 30-60-90 plans", value=False)
+
+# --------------------------
+# Load data
+# --------------------------
+if file:
+    df_raw = pd.read_csv(file)
+elif use_sample:
+    df_raw = pd.read_csv("sample_data/team_members_sample.csv")
+else:
+    st.stop()
+
+st.subheader("📥 Input Data")
+st.dataframe(df_raw, use_container_width=True)
+
+# --------------------------
+# Calculate Results
+# --------------------------
+df = df_raw.copy()
+df["ReadinessScore"] = df.apply(score, axis=1)
+df["SuggestedNextRole"] = df.apply(suggest_next_role, axis=1)
+df["RecommendedActions"] = df.apply(suggest_actions, axis=1)
+
+if use_llm:
+    df["LLMPlan"] = df.apply(generate_llm_plan, axis=1)
+else:
+    df["LLMPlan"] = "LLM disabled"
+
+# --------------------------
+# Summary Output
+# --------------------------
+st.subheader("📊 Team Summary")
+st.dataframe(
+    df[["Name", "CurrentRole", "YearsExperience", "ReadinessScore",
+        "SuggestedNextRole"]],
+    use_container_width=True
+)
+
+# --------------------------
+# Detailed Cards
+# --------------------------
+st.subheader("🧠 Detailed Recommendations")
+
+for _, row in df.iterrows():
+    with st.expander(f"{row['Name']} — {row['SuggestedNextRole']}"):
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.markdown("### Recommended Actions")
+            st.write(row["RecommendedActions"])
+
+        with col2:
+            st.markdown("### 30-60-90 AI Plan")
+            st.write(row["LLMPlan"])
+
+# --------------------------
+# Dashboard
+# --------------------------
+st.markdown("---")
+st.header("📈 Team Dashboard")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    fig = px.bar(df, x="Name", y="ReadinessScore", text="ReadinessScore")
+    st.plotly_chart(fig, use_container_width=True)
+
+with col2:
+    count = df["SuggestedNextRole"].value_counts().reset_index()
+    count.columns = ["Role", "Count"]
+    fig2 = px.pie(count, names="Role", values="Count")
+    st.plotly_chart(fig2, use_container_width=True)
+
+
+# --------------------------
+# Download results
+# --------------------------
+buffer = StringIO()
+df.to_csv(buffer, index=False)
+
+st.download_button(
+    "📥 Download Full Results CSV",
+    buffer.getvalue(),
+    "career_progression_results.csv",
+    "text/csv"
+)