Create app.py

app.py

@@ -0,0 +1,123 @@
+import streamlit as st
+
+# --- App Configuration ---
+st.set_page_config(
+    page_title="Basic Python Sentiment Analyzer",
+    page_icon="✍️",
+    layout="centered",
+    initial_sidebar_state="auto"
+)
+
+# --- Define Sentiment Keywords (Pure Python Logic) ---
+# These are very basic lists for demonstration purposes.
+# A real-world rule-based system would be much more extensive and nuanced.
+POSITIVE_KEYWORDS = [
+    "good", "great", "excellent", "amazing", "fantastic", "love", "happy",
+    "joy", "wonderful", "positive", "awesome", "beautiful", "perfect", "like",
+    "enjoy", "best", "super", "nice", "pleased", "delightful", "brilliant"
+]
+
+NEGATIVE_KEYWORDS = [
+    "bad", "terrible", "horrible", "awful", "hate", "sad", "unhappy",
+    "poor", "negative", "disappointing", "worst", "ugly", "frustrating",
+    "dislike", "annoying", "miserable", "stressful", "difficult", "problem",
+    "fail", "ruin", "never"
+]
+
+# --- Sentiment Analysis Function (Pure Python) ---
+def analyze_sentiment_basic(text):
+    """
+    Performs a very basic sentiment analysis based on predefined positive and negative keywords.
+    This function does not use any external NLP models or libraries.
+    """
+    if not text:
+        return "Neutral", 0, 0 # Return neutral if no text
+
+    text_lower = text.lower()
+    positive_count = 0
+    negative_count = 0
+
+    # Count positive keywords
+    for keyword in POSITIVE_KEYWORDS:
+        positive_count += text_lower.count(keyword)
+
+    # Count negative keywords
+    for keyword in NEGATIVE_KEYWORDS:
+        negative_count += text_lower.count(keyword)
+
+    # Determine sentiment
+    if positive_count > negative_count:
+        return "Positive", positive_count, negative_count
+    elif negative_count > positive_count:
+        return "Negative", positive_count, negative_count
+    else:
+        return "Neutral", positive_count, negative_count
+
+# --- Streamlit UI ---
+st.title("✍️ Basic Python Sentiment Analyzer")
+st.markdown(
+    """
+    This app performs sentiment analysis using only pure Python, based on a simple keyword matching approach.
+    It's a demonstration of a rule-based system without external NLP models.
+    """
+)
+
+# Text input from the user
+user_input = st.text_area(
+    "Enter your text here:",
+    "This is a good example, but it could be even better.",
+    height=150
+)
+
+# Analyze button
+if st.button("Analyze Sentiment"):
+    if user_input:
+        sentiment, pos_count, neg_count = analyze_sentiment_basic(user_input)
+
+        st.subheader("Analysis Result:")
+
+        # Display result with appropriate styling
+        if sentiment == "Positive":
+            st.success(f"**Sentiment:** Positive 😊")
+        elif sentiment == "Negative":
+            st.error(f"**Sentiment:** Negative 😠")
+        else:
+            st.warning(f"**Sentiment:** Neutral 😐")
+
+        st.info(f"Positive keyword matches: {pos_count}")
+        st.info(f"Negative keyword matches: {neg_count}")
+
+        st.markdown("---")
+        st.write(f"**Original Text:**")
+        st.write(f"> {user_input}")
+    else:
+        st.warning("Please enter some text to analyze.")
+
+# Custom CSS for styling
+st.markdown(
+    """
+    <style>
+    .stButton>button {
+        background-color: #007bff; /* Blue color for the button */
+        color: white;
+        padding: 10px 20px;
+        border-radius: 8px;
+        border: none;
+        cursor: pointer;
+        font-size: 16px;
+        box-shadow: 0 4px 8px 0 rgba(0,0,0,0.2);
+        transition: 0.3s;
+    }
+    .stButton>button:hover {
+        background-color: #0056b3; /* Darker blue on hover */
+        box-shadow: 0 8px 16px 0 rgba(0,0,0,0.2);
+    }
+    .stTextArea>div>div>textarea {
+        border-radius: 8px;
+        border: 1px solid #ccc;
+        padding: 10px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)