Create app.py

app.py

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+import gradio as gr
+import tweepy
+import pandas as pd
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
+from transformers import pipeline
+
+# Hugging Face Models
+sentiment_analyzer = pipeline("sentiment-analysis")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Twitter API Setup (replace with your Bearer Token)
+BEARER_TOKEN = "AAAAAAAAAAAAAAAAAAAAAN3g3wEAAAAA33Fzyb2P1rQzFwmXPIh4OHIw7e8%3DJY5zPrXhmzlht200jSaA8dgQixPX6idTvk2HWX0LgKwruozAsC"
+client = tweepy.Client(bearer_token=BEARER_TOKEN)
+
+# Function: Fetch Tweets
+def fetch_tweets(username, count=50):
+    tweets = client.get_users_tweets(
+        id=client.get_user(username=username).data.id,
+        max_results=min(count, 100)
+    )
+    texts = [t.text for t in tweets.data] if tweets.data else []
+    df = pd.DataFrame(texts, columns=["text"])
+    return df
+
+# Function: Load file (CSV/XLSX)
+def load_file(file):
+    if file.name.endswith(".csv"):
+        return pd.read_csv(file.name)
+    elif file.name.endswith(".xlsx"):
+        return pd.read_excel(file.name)
+    else:
+        return pd.DataFrame(columns=["text"])
+
+# Function: Clean text
+def clean_text(df):
+    df["cleaned_text"] = (
+        df["text"].astype(str)
+        .str.replace(r"http\S+", "", regex=True)
+        .str.replace(r"@\w+", "", regex=True)
+        .str.replace(r"[^A-Za-z0-9\s]", "", regex=True)
+        .str.strip()
+    )
+    return df
+
+# Function: Sentiment, Summary & WordCloud
+def analyze_data(df):
+    if df.empty:
+        return "No data found", None, None
+    
+    df = clean_text(df)
+
+    # Sentiment
+    df["sentiment"] = df["cleaned_text"].apply(
+        lambda x: sentiment_analyzer(x[:512])[0]["label"] if len(x) > 0 else "neutral"
+    )
+
+    # Summary (combine text for summarization)
+    full_text = " ".join(df["cleaned_text"].tolist())[:3000]
+    summary = summarizer(full_text, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
+
+    # WordCloud
+    text_for_wc = " ".join(df["cleaned_text"].tolist())
+    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(text_for_wc)
+    plt.figure(figsize=(8, 4))
+    plt.imshow(wordcloud, interpolation="bilinear")
+    plt.axis("off")
+    plt.tight_layout()
+    plt.savefig("wordcloud.png")
+
+    return summary, df, "wordcloud.png"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📊 Twitter & File Sentiment Analysis Prototype")
+
+    with gr.Tab("Fetch Tweets"):
+        username = gr.Textbox(label="Twitter Username (without @)")
+        count = gr.Slider(10, 100, value=50, step=10, label="Number of Tweets")
+        btn_fetch = gr.Button("Fetch & Analyze")
+        summary_out = gr.Textbox(label="Summary")
+        df_out = gr.Dataframe()
+        img_out = gr.Image()
+
+    with gr.Tab("Upload File"):
+        file_in = gr.File(label="Upload CSV/XLSX")
+        btn_file = gr.Button("Analyze File")
+        summary_out2 = gr.Textbox(label="Summary")
+        df_out2 = gr.Dataframe()
+        img_out2 = gr.Image()
+
+    # Actions
+    btn_fetch.click(
+        lambda u, c: analyze_data(fetch_tweets(u, c)),
+        inputs=[username, count],
+        outputs=[summary_out, df_out, img_out],
+    )
+    btn_file.click(
+        lambda f: analyze_data(load_file(f)),
+        inputs=[file_in],
+        outputs=[summary_out2, df_out2, img_out2],
+    )
+
+demo.launch()