Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import random
+import time
+import plotly.express as px
+import requests
+import os
+
+# ---------------------------
+# Load Hugging Face sentiment model
+# ---------------------------
+sentiment_model = pipeline("sentiment-analysis")
+
+# ---------------------------
+# Gemini API Config
+# ---------------------------
+GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+def gemini_sentiment(text):
+    """Use Gemini API for sentiment analysis"""
+    if not GEMINI_API_KEY:
+        return {"label": "NEUTRAL", "score": 0.0}
+
+    url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"
+    headers = {"Content-Type": "application/json", "X-goog-api-key": GEMINI_API_KEY}
+    payload = {
+        "contents": [{"parts": [{"text": f"Classify sentiment of this text as Positive, Negative or Neutral:\n\n{text}"}]}]
+    }
+    resp = requests.post(url, headers=headers, json=payload)
+    if resp.status_code == 200:
+        ai_text = resp.json()["candidates"][0]["content"]["parts"][0]["text"].strip().lower()
+        if "positive" in ai_text:
+            return {"label": "POSITIVE", "score": 0.9}
+        elif "negative" in ai_text:
+            return {"label": "NEGATIVE", "score": 0.9}
+        else:
+            return {"label": "NEUTRAL", "score": 0.5}
+    return {"label": "NEUTRAL", "score": 0.0}
+
+# ---------------------------
+# Mock social posts generator
+# ---------------------------
+def fetch_posts(hashtag, n=20):
+    samples = [
+        f"I love {hashtag}! It's amazing ❤️",
+        f"{hashtag} is the worst thing ever 😡",
+        f"Not sure how I feel about {hashtag} 🤔",
+        f"Super excited about {hashtag} 🔥",
+        f"{hashtag} totally failed expectations 😞",
+        f"People are talking about {hashtag} everywhere 🌍",
+        f"I'm disappointed with {hashtag} 💔",
+        f"{hashtag} campaign is the best thing this year 🎉",
+    ]
+    return [random.choice(samples) for _ in range(n)]
+
+# ---------------------------
+# Analyzer function
+# ---------------------------
+def analyze_hashtag(hashtag, n_posts=20, chart_type="Bar", use_gemini=False):
+    posts = fetch_posts(hashtag, n_posts)
+    results = []
+
+    for p in posts:
+        if use_gemini:
+            result = gemini_sentiment(p)
+        else:
+            result = sentiment_model(p)[0]
+        results.append({"Post": p, "Sentiment": result["label"], "Confidence": round(result["score"], 2)})
+        time.sleep(0.05)  # simulate streaming
+
+    df = pd.DataFrame(results)
+
+    # Count distribution
+    sentiment_counts = df["Sentiment"].value_counts().reset_index()
+    sentiment_counts.columns = ["Sentiment", "Count"]
+
+    # Plotly Graph
+    if chart_type == "Bar":
+        fig = px.bar(sentiment_counts, x="Sentiment", y="Count", color="Sentiment",
+                     title=f"Sentiment Distribution for {hashtag}")
+    elif chart_type == "Pie":
+        fig = px.pie(sentiment_counts, names="Sentiment", values="Count",
+                     title=f"Sentiment Share for {hashtag}")
+    else:  # Line chart (simulate rolling trend)
+        fig = px.line(sentiment_counts, x="Sentiment", y="Count", markers=True,
+                      title=f"Sentiment Rolling Trend for {hashtag}")
+
+    return df, fig
+
+# ---------------------------
+# Gradio UI
+# ---------------------------
+with gr.Blocks(css=".footer {text-align:center; font-size:16px; color:#ff66cc; font-weight:bold; animation: glow 1.5s ease-in-out infinite alternate;} @keyframes glow { from { text-shadow: 0 0 10px #ff66cc; } to { text-shadow: 0 0 20px #ff33aa; }}") as demo:
+    gr.Markdown(
+        """
+        <div style='text-align:center; padding: 20px; background: linear-gradient(90deg, #1e3c72, #2a5298); color: white; border-radius: 12px;'>
+            <h1>📊 Social Media Sentiment Analyzer</h1>
+            <p>Stream posts • Analyze moods • Visualize trends</p>
+        </div>
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            hashtag = gr.Textbox(label="Enter Hashtag", placeholder="#YourCampaign")
+            n_posts = gr.Slider(5, 50, step=5, value=20, label="Number of Posts")
+            chart_type = gr.Dropdown(["Bar", "Pie", "Line"], value="Bar", label="Choose Visualization")
+            use_gemini = gr.Checkbox(label="Use Gemini Advanced Analysis", value=False)
+            btn = gr.Button("🚀 Run Analysis", variant="primary")
+
+        with gr.Column(scale=2):
+            output_table = gr.Dataframe(label="Posts & Sentiments", wrap=True)
+            output_plot = gr.Plot(label="Visualization")
+
+    btn.click(fn=analyze_hashtag, inputs=[hashtag, n_posts, chart_type, use_gemini], outputs=[output_table, output_plot])
+
+    gr.HTML("<div class='footer'>✨ Made with 💜 by Kavya</div>")
+
+demo.launch()