Update app.py

app.py

@@ -1,51 +1,51 @@
 import streamlit as st
+import openai
 import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, classification_report
-from sklearn.preprocessing import StandardScaler
-
-st.set_page_config(page_title="Random Forest Diabetes Classifier", layout="centered")
-st.title("👨🏻‍💻Dynamic Code Generating ChatBot🤖")
-
-uploaded_file = st.file_uploader("📂 Upload your  CSV dataset", type=["csv"])
-
-if uploaded_file:
-    df = pd.read_csv(uploaded_file)
-    st.success("✅ File loaded successfully!")
-    st.write("### Preview of Dataset:")
-    st.dataframe(df.head())
-
-    all_columns = df.columns.tolist()
-
-    target_column = st.selectbox("🎯 Select the target column (diabetes outcome)", all_columns)
-    feature_columns = st.multiselect("🛠️ Select feature columns", [col for col in all_columns if col != target_column])
-
-    if st.button("🚀 Run Random Forest Classifier"):
+import subprocess
+import os
+import io
+import contextlib
+
+# Setup OpenAI
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+st.set_page_config(page_title="🧠 Build Any Model", layout="centered")
+st.title("🤖 Dynamic Code Generator Chatbot")
+
+prompt = st.text_input("🧠 Tell me what you want to build:")
+
+uploaded_file = st.file_uploader("📂 Upload your dataset (CSV)", type=["csv"])
+
+if st.button("🚀 Generate and Run"):
+    if prompt and uploaded_file:
+        df = pd.read_csv(uploaded_file)
+        st.success("✅ File loaded successfully.")
+        
+        # Call OpenAI to generate code
+        system_prompt = "You are a Python code generator. Only output clean, executable Python code inside one code block. No explanation."
+        client = openai.OpenAI()
+        response = client.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.3,
+        )
+        code = response.choices[0].message.content
+        if "```python" in code:
+            code = code.split("```python")[-1].split("```")[0].strip()
+        
+        st.code(code, language="python")
+        
+        # Execute the generated code
+        output_buffer = io.StringIO()
         try:
-            X = df[feature_columns]
-            y = df[target_column]
-
-            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-            scaler = StandardScaler()
-            X_train = scaler.fit_transform(X_train)
-            X_test = scaler.transform(X_test)
-
-            model = RandomForestClassifier(n_estimators=100, random_state=42)
-            model.fit(X_train, y_train)
-            y_pred = model.predict(X_test)
-
-            accuracy = accuracy_score(y_test, y_pred)
-            report = classification_report(y_test, y_pred, output_dict=False)
-
-            st.write("### ✅ Accuracy:")
-            st.write(f"{accuracy * 100:.2f}%")
-
-            st.write("### 📋 Classification Report:")
-            st.code(report)
-
+            with contextlib.redirect_stdout(output_buffer):
+                exec(code, {"df": df})
+            st.success("✅ Code executed successfully.")
+            st.text(output_buffer.getvalue())
         except Exception as e:
-            st.error(f"❌ An error occurred: {e}")
-else:
-    st.info("👈 Upload a CSV file to begin.")
+            st.error(f"❌ Error during execution: {e}")
+    else:
+        st.warning("⚠️ Please upload a dataset and enter a prompt.")