Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,95 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-"""
-# Welcome to Streamlit!
+# ==============================
+# 🔐 HIDE STREAMLIT MENU
+# ==============================
+st.markdown("""
+<style>
+#MainMenu {visibility: hidden;}
+header {visibility: hidden;}
+footer {visibility: hidden;}
+.stDeployButton {display:none;}
+</style>
+""", unsafe_allow_html=True)
+
+# ==============================
+# PAGE CONFIG
+# ==============================
+st.set_page_config(page_title="💻 AI Code Generator", layout="wide")
+
+# ==============================
+# LOAD MODEL
+# ==============================
+@st.cache_resource
+def load_model():
+    model_name = "codellama/CodeLlama-7b-Instruct-hf"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# ==============================
+# CODE GENERATION FUNCTION
+# ==============================
+def generate_code(prompt, language):
+
+    full_prompt = f"""
+You are an expert {language} developer.
+
+Write clean, optimized, production-ready code.
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+Task:
+{prompt}
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+Rules:
+- Only return code
+- No explanation
 """
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        temperature=0.2,
+        top_p=0.9
+    )
+
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return result.replace(full_prompt, "").strip()
+
+# ==============================
+# UI
+# ==============================
+st.title("💻 AI Code Generator")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    user_prompt = st.text_area("Describe your task", height=200)
+
+with col2:
+    language = st.selectbox(
+        "Select Programming Language",
+        ["Python", "JavaScript", "SQL", "Java", "C++", "HTML", "CSS"]
+    )
+
+if st.button("Generate Code"):
+    if not user_prompt.strip():
+        st.warning("Please enter a task")
+    else:
+        with st.spinner("Generating code..."):
+            code = generate_code(user_prompt, language)
+
+            st.success("✅ Generated Code")
+            st.code(code, language=language.lower())