Update app.py

app.py

@@ -1,23 +1,25 @@
-# Install datasets library
-!pip install -U datasets
-
-# Load Sentiment140 dataset
-from datasets import load_dataset
-dataset = load_dataset("sentiment140")
-
-# Convert to pandas
+import gradio as gr
 import pandas as pd
-df = dataset["train"].to_pandas()
-df.head()
-# Drop null values in text and sentiment
-df.dropna(subset=["text", "sentiment"], inplace=True)
+import re
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+from transformers import pipeline, set_seed
+import numpy as np
 
-# Filter tweets with reasonable length
-df["text_length"] = df["text"].apply(len)
-df = df[(df["text_length"] >= 5) & (df["text_length"] <= 280)]
+# -------------------------------
+# 1. Load and clean dataset
+# -------------------------------
+@st.cache_resource
+def load_and_prepare_data():
+    dataset = load_dataset("sentiment140")
+    df = dataset["train"].to_pandas()
+    df.dropna(subset=["text", "sentiment"], inplace=True)
+    df["text_length"] = df["text"].apply(len)
+    df = df[(df["text_length"] >= 5) & (df["text_length"] <= 280)]
+    df["clean_text"] = df["text"].apply(clean_text)
+    return df
 
-# Clean the text
-import re
 def clean_text(text):
     text = text.lower()
     text = re.sub(r"http\S+", "", text)
@@ -27,83 +29,31 @@ def clean_text(text):
     text = re.sub(r"\s+", " ", text).strip()
     return text
 
-df["clean_text"] = df["text"].apply(clean_text)
-df[["text", "clean_text"]].head()
-# Convert sentiment labels from numbers to text
-def map_sentiment(label):
-    return "negative" if label == 0 else "neutral" if label == 2 else "positive"
-
-df["sentiment_label"] = df["sentiment"].apply(map_sentiment)
-df["sentiment_label"].value_counts()
-# Save for future use
-df[["clean_text", "sentiment_label"]].to_csv("cleaned_sentiment140.csv", index=False)
-print("Cleaned data saved!")
-!pip install -U sentence-transformers
-from sentence_transformers import SentenceTransformer
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-
-# Use a small sample for speed (feel free to increase)
+# Load data once
+df = load_and_prepare_data()
 sample_df = df.sample(5000, random_state=42).reset_index(drop=True)
 texts = sample_df["clean_text"].tolist()
 
-# Load 3 different embedding models
-models = {
-    "MiniLM": SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2"),
-    "MPNet": SentenceTransformer("sentence-transformers/all-mpnet-base-v2"),
-    "DistilRoBERTa": SentenceTransformer("sentence-transformers/paraphrase-distilroberta-base-v1")
-}
-# Compute and compare similarity for one test input
-test_input = "I am so happy with this product"
-
-def get_top3_similarities(model, texts, test_input):
-    text_embeddings = model.encode(texts, show_progress_bar=True)
-    input_embedding = model.encode([test_input])
+# -------------------------------
+# 2. Load models
+# -------------------------------
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+generator = pipeline("text-generation", model="distilgpt2")
+set_seed(42)
+
+# -------------------------------
+# 3. Helper functions
+# -------------------------------
+def get_top3_similarities(text_input):
+    text_embeddings = embedding_model.encode(texts, show_progress_bar=False)
+    input_embedding = embedding_model.encode([text_input])
     similarities = cosine_similarity(input_embedding, text_embeddings)[0]
     top_indices = similarities.argsort()[-3:][::-1]
-    return [(i, texts[i], similarities[i]) for i in top_indices]
-
-# Try each model
-results = {}
-for name, model in models.items():
-    print(f"\n🔎 Top 3 results from: {name}")
-    top3 = get_top3_similarities(model, texts, test_input)
-    for rank, (idx, text, score) in enumerate(top3, start=1):
-        print(f"{rank}. [{score:.4f}] {text}")
-    results[name] = top3
-!pip install -U transformers
-from transformers import pipeline, set_seed
-
-# Load small GPT-2 model for text generation
-generator = pipeline("text-generation", model="distilgpt2")
-set_seed(42)  # reproducible results
-# Example user input
-test_input = "I'm feeling amazing about our product launch!"
-# Generate synthetic tweets
-synthetic_outputs = generator(
-    test_input,
-    max_length=50,
-    num_return_sequences=10,
-    do_sample=True,
-    temperature=0.9
-)
-
-# Extract just the generated text
-generated_tweets = [output["generated_text"].strip() for output in synthetic_outputs]
-for i, tweet in enumerate(generated_tweets, 1):
-    print(f"{i}. {tweet}\n")
-from sentence_transformers import SentenceTransformer
-
-# Load your best model again (MiniLM is a good choice)
-embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-# Embed input and generated tweets
-input_vec = embedding_model.encode([test_input])
-gen_vecs = embedding_model.encode(generated_tweets)
-
-# Compute similarity and select best
-from sklearn.metrics.pairwise import cosine_similarity
-similarities = cosine_similarity(input_vec, gen_vecs)[0]
-top_index = similarities.argmax()
-best_generated = generated_tweets[top_index]
-
-print(f"✅ Best AI-generated tweet:\n[{similarities[top_index]:.4f}] {best_generated}")
+    return [texts[i] for i in top_indices]
+
+def generate_best_tweet(text_input):
+    synthetic_outputs = generator(
+        text_input,
+        max_length=50,
+        num_return_sequences=10,
+        do_samp_