Initial commit

README.md

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----
-title: Ml Code Generator
-emoji: 📊
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.43.2
-app_file: app.py
-pinned: false
-short_description: SteamLit app allowing users to generate ML model code
----
+# AI Code Generator (Streamlit)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Generate AI model training code in Python with **one click**!  
+Simply choose a **model, task, and problem**, and the **code will be generated** for you.
+
+## Features
+✅ Supports KNN, SVM, Random Forest, Decision Tree, and Perceptron  
+✅ Choose between **classification** and **regression**  
+✅ Select problems like **Spam Detection, House Price Prediction, Fraud Detection**, etc.  
+✅ One-click **download** `.py` and `.ipynb` files  
+
+## Installation & Run
+```bash
+pip install -r requirements.txt
+streamlit run app.py

app.py

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+import streamlit as st
+import pandas as pd
+import textwrap
+
+# Sidebar UI
+st.sidebar.title("AI Code Generator 🧠")
+st.sidebar.markdown("Generate AI models instantly!")
+
+# Model Selection
+model_options = ["KNN", "SVM", "Random Forest", "Decision Tree", "Perceptron"]
+model = st.sidebar.selectbox("Choose a Model:", model_options)
+
+# Task Selection
+task_options = ["Classification", "Regression"]
+task = st.sidebar.selectbox("Choose a Task:", task_options)
+
+# Problem Selection based on Task and Model
+problems = {
+    "Classification": {
+        "KNN": ["Disease Prediction", "Spam Detection"],
+        "SVM": ["Image Recognition", "Text Classification"],
+        "Random Forest": ["Fraud Detection", "Customer Segmentation"],
+        "Decision Tree": ["Loan Approval", "Churn Prediction"],
+        "Perceptron": ["Handwritten Digit Recognition", "Sentiment Analysis"]
+    },
+    "Regression": {
+        "KNN": ["House Price Prediction", "Stock Prediction"],
+        "SVM": ["Sales Forecasting", "Stock Market Trends"],
+        "Random Forest": ["Energy Consumption", "Patient Survival Prediction"],
+        "Decision Tree": ["House Price Estimation", "Revenue Prediction"],
+        "Perceptron": ["Weather Forecasting", "Traffic Flow Prediction"]
+    }
+}
+
+problem = st.sidebar.selectbox("Choose a Problem:", problems[task][model])
+
+# Generate AI Model Code
+def generate_code(model, task, problem):
+    model_mapping = {
+        "KNN": "KNeighborsClassifier" if task == "Classification" else "KNeighborsRegressor",
+        "SVM": "SVC" if task == "Classification" else "SVR",
+        "Random Forest": "RandomForestClassifier" if task == "Classification" else "RandomForestRegressor",
+        "Decision Tree": "DecisionTreeClassifier" if task == "Classification" else "DecisionTreeRegressor",
+        "Perceptron": "Perceptron" if task == "Classification" else "Perceptron"
+    }
+    
+    selected_model = model_mapping[model]
+
+    template = f"""
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.{model.lower()} import {selected_model}
+
+# Load Dataset (Replace with your own dataset)
+data = pd.read_csv('dataset.csv')
+X = data.iloc[:, :-1]  # Features
+y = data.iloc[:, -1]   # Target
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize Features (if needed)
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Model Initialization
+model = {selected_model}()
+
+# Training the model
+model.fit(X_train, y_train)
+
+# Evaluate Model
+accuracy = model.score(X_test, y_test)
+print("Model Accuracy:", accuracy)
+"""
+    return textwrap.dedent(template)
+
+code = generate_code(model, task, problem)
+st.code(code, language="python")
+
+# Download Buttons
+st.download_button("🐍 Download (.py)", code, "ai_model.py")
+st.download_button("📓 Download (.ipynb)", code, "ai_model.ipynb")
+
+st.success("Code generated! Download and do magic! ✨")

dataset.csv

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+# Fake dataset for AI Code Generator
+# You can replace this with your own dataset
+
+feature1,feature2,feature3,feature4,target
+34,180,1,50000,0
+25,165,0,60000,1
+40,175,1,55000,0
+30,170,0,62000,1
+45,185,1,58000,0
+28,160,0,57000,1
+35,178,1,53000,0
+50,190,1,49000,1
+23,158,0,61000,0
+38,172,1,56000,1

requirements.txt

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+streamlit
+scikit-learn
+pandas
+numpy