init

app.py

@@ -0,0 +1,102 @@
+import gradio as gr
+import pandas as pd
+from collections import Counter
+import re
+
+def ana(txt):
+    """Analyze text and return comprehensive stats in dataframes"""
+    if not txt.strip():
+        return "Enter text!", None, None, None, None
+    
+    wds = txt.split()
+    chs = len(txt)
+    wdc = len(wds)
+    sns = [s.strip() for s in re.split(r'[.!?]+', txt) if s.strip()]
+    snc = len(sns)
+    avg = wdc / max(1, snc)
+    rdm = wdc / 200
+    wrd = sum(len(w) for w in wds) / max(1, wdc)  # avg word length
+    syl = sum(max(1, len(re.findall(r'[aeiouy]+', w.lower()))) for w in wds)  # rough syllable count
+    fre = 206.835 - 1.015 * avg - 84.6 * (syl / max(1, wdc))  # Flesch Reading Ease
+    fkg = 0.39 * avg + 11.8 * (syl / max(1, wdc)) - 15.59  # Flesch-Kincaid Grade
+
+    # Add to stats dataframe
+    st = pd.DataFrame({
+        '📊 Metric': ['Characters', 'Words', 'Sentences', 'Avg Words/Sentence', 
+                    'Reading Time (min)', 'Readability Score', 'Grade Level'],
+        '📈 Value': [chs, wdc, snc, f'{avg:.1f}', f'{rdm:.1f}', 
+                    f'{fre:.1f}', f'{fkg:.1f}']
+    })
+    
+    # Word frequency (1-grams)
+    wfq = Counter([w.lower().strip('.,!?;:"()[]') for w in wds if w.strip('.,!?;:"()[]')])
+    wf = pd.DataFrame(wfq.most_common(15), columns=['🔤 Word', '📊 Count'])
+    
+    # Bi-grams
+    bgr = [f"{wds[i].lower()} {wds[i+1].lower()}" for i in range(len(wds)-1)]
+    bgc = Counter(bgr)
+    bg = pd.DataFrame(bgc.most_common(15), columns=['🔤 Bi-gram', '📊 Count'])
+    
+    # Tri-grams
+    tgr = [f"{wds[i].lower()} {wds[i+1].lower()} {wds[i+2].lower()}" for i in range(len(wds)-2)]
+    tgc = Counter(tgr)
+    tg = pd.DataFrame(tgc.most_common(15), columns=['🔤 Tri-gram', '📊 Count'])
+    
+    # Sentence analysis with actual sentences
+    sls = []
+    for i, sn in enumerate(sns, 1):
+        swc = len(sn.split())
+        sls.append({
+            '📝 #': i,
+            '📏 Words': swc,
+            '💬 Sentence': sn
+        })
+    sl = pd.DataFrame(sls)
+    
+    # Character distribution
+    chd = Counter(txt.lower())
+    let = {c: chd[c] for c in chd if c.isalpha()}
+    let = dict(sorted(let.items(), key=lambda x: x[1], reverse=True)[:20])
+    ch = pd.DataFrame(list(let.items()), columns=['🔤 Letter', '📊 Count'])
+    
+    return st, wf, bg, tg, sl, ch
+
+# Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft(), css="""
+    .gradio-container {background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);}
+    h1, h3 {color: white !important;}
+    """) as app:
+    
+    gr.Markdown("# 🚀 Text Analyzer Pro\n### Comprehensive text analysis with n-grams!")
+    
+    inp = gr.Textbox(label="📝 Enter Your Text", placeholder="Type or paste text...", lines=8)
+    btn = gr.Button("🔍 Analyze", variant="primary", size="lg")
+    
+    with gr.Row():
+        o1 = gr.Dataframe(label="📊 Overall Statistics", interactive=False)
+        o2 = gr.Dataframe(label="🎨 Character Distribution", interactive=False)
+    
+    gr.Markdown("### 🔤 N-Gram Analysis")
+    with gr.Row():
+        o3 = gr.Dataframe(label="1-Grams (Words)", interactive=False)
+        o4 = gr.Dataframe(label="2-Grams (Phrases)", interactive=False)
+        o5 = gr.Dataframe(label="3-Grams (Phrases)", interactive=False)
+    
+    gr.Markdown("### 📈 Sentence Analysis")
+    o6 = gr.Dataframe(label="📝 Sentences with Length", interactive=False, wrap=True)
+    
+    btn.click(fn=ana, inputs=inp, outputs=[o1, o3, o4, o5, o6, o2])
+    
+    gr.Examples(
+        examples=[
+            ["The quick brown fox jumps over the lazy dog. This is a test sentence. Testing is important!"],
+            ["Machine learning is transforming technology. Artificial intelligence powers innovation. Deep learning drives progress."]
+        ],
+        inputs=inp,
+        label="💡 Try These Examples"
+    )
+    
+    gr.Markdown("---\n*Built with ❤️ using Gradio | All variables ≤ 3 chars*")
+
+if __name__ == "__main__":
+    app.launch()