Upload 2 files

app.py

@@ -0,0 +1,159 @@
+import pandas as pd
+import statsmodels.api as sm
+import streamlit as st
+from datetime import datetime
+import os
+
+# ---- App title ----
+st.title("Mini Stata - Simplified Version")
+
+# ---- Student login ----
+st.subheader("Student Login")
+student_name = st.text_input("Enter your name:")
+student_id = st.text_input("Enter your student ID:")
+
+if student_name and student_id:
+    logfile = f"log_{student_id}.csv"
+
+    # Ensure log file exists
+    if not os.path.exists(logfile):
+        pd.DataFrame(columns=["timestamp", "student_name", "student_id", "command", "result"]).to_csv(logfile, index=False)
+
+    st.success(f"Logged in as {student_name} (ID: {student_id})")
+
+    # ---- File upload ----
+    uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.success("File uploaded successfully!")
+    else:
+        st.info("No file uploaded. Using default sample dataset.")
+        df = pd.DataFrame({
+            "mpg": [21, 22, 18, 30],
+            "weight": [2500, 2800, 3200, 2100],
+            "cyl": [4, 4, 6, 4],
+            "horsepower": [90, 95, 110, 80]
+        })
+
+    # ---- summarize ----
+    def summarize(var=None):
+        try:
+            if var is None:
+                return df.describe().T
+            elif var in df.columns:
+                return df[var].describe().to_frame().T
+            else:
+                return f"Variable '{var}' not found."
+        except Exception as e:
+            return f"Error in summarize: {e}"
+
+    # ---- browse ----
+    def browse(n=None):
+        try:
+            if n is None:
+                return df
+            return df.head(n)
+        except Exception as e:
+            return f"Error in browse: {e}"
+
+    # ---- tab ----
+    def tab(var):
+        try:
+            if var not in df.columns:
+                return f"Variable '{var}' not found."
+            return df[var].value_counts().to_frame("Frequency")
+        except Exception as e:
+            return f"Error in tab: {e}"
+
+    # ---- reg (simplified like Stata) ----
+    def reg(dep_var, indep_vars):
+        try:
+            if dep_var not in df.columns:
+                return f"Dependent variable '{dep_var}' not found."
+            for v in indep_vars:
+                if v not in df.columns:
+                    return f"Independent variable '{v}' not found."
+
+            X = sm.add_constant(df[indep_vars])
+            y = df[dep_var]
+            model = sm.OLS(y, X).fit()
+
+            # Create clean results table
+            results_table = pd.DataFrame({
+                'Variable': model.params.index,
+                'Coef.': model.params.values.round(4),
+                'Std. Err.': model.bse.values.round(4),
+                't': model.tvalues.values.round(3),
+                'P>|t|': model.pvalues.values.round(3)
+            })
+
+            # Display concise summary
+            summary_stats = f"Number of obs = {int(model.nobs)}    R-squared = {model.rsquared:.3f}"
+            return results_table, summary_stats
+        except Exception as e:
+            return f"Error in regression: {e}"
+
+    # ---- Command parser ----
+    def run_command(cmd):
+        parts = cmd.strip().split()
+        if not parts:
+            return "No command entered."
+        command = parts[0].lower()
+        args = parts[1:]
+
+        if command == "summarize":
+            return summarize(args[0]) if args else summarize()
+        elif command == "browse":
+            return browse()
+        elif command == "tab":
+            if not args:
+                return "Usage: tab varname"
+            return tab(args[0])
+        elif command == "reg":
+            if len(args) < 2:
+                return "Usage: reg depvar indepvar1 indepvar2 ..."
+            return reg(args[0], args[1:])
+        else:
+            return f"Unknown command: '{command}'. Available commands: summarize, browse, tab, reg."
+
+    # ---- Interface ----
+    st.markdown("""
+    ### Available commands
+    - `summarize`
+    - `summarize mpg`
+    - `browse`
+    - `tab cyl`
+    - `reg mpg weight horsepower`
+    """)
+
+    cmd = st.text_input("Enter command:")
+
+    if st.button("Run"):
+        result = run_command(cmd)
+
+        # Log
+        log_entry = {
+            "timestamp": datetime.now().isoformat(),
+            "student_name": student_name,
+            "student_id": student_id,
+            "command": cmd,
+            "result": str(result)[:500]
+        }
+        pd.DataFrame([log_entry]).to_csv(logfile, mode="a", header=False, index=False)
+
+        # Display result
+        if isinstance(result, tuple):  # regression output
+            table, stats = result
+            st.text(stats)
+            st.table(table)
+        elif isinstance(result, pd.DataFrame):
+            st.dataframe(result)
+        else:
+            st.text(result)
+
+    if st.button("Download My Log"):
+        with open(logfile, "r") as f:
+            st.download_button("Click to download", f, file_name=logfile, mime="text/csv")
+
+else:
+    st.warning("Please enter name and student ID to start.")

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+pandas
+statsmodels