Update app.py

app.py

@@ -1,145 +1,127 @@
 import os
 import random
-import gradio as gr
+import streamlit as st
+import pandas as pd
 import plotly.express as px
-from transformers import pipeline
 import google.generativeai as genai
+from transformers import pipeline
 
-# ----------------- CONFIG -----------------
-# Load Gemini API key from environment secrets
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-
-if GEMINI_API_KEY:
-    genai.configure(api_key=GEMINI_API_KEY)
-    gemini_model = genai.GenerativeModel("gemini-1.5-flash")
-else:
-    gemini_model = None
-
-# Hugging Face sentiment pipeline
+# ========== API KEYS ==========
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 sentiment_pipeline = pipeline("sentiment-analysis")
 
-# ----------------- FUNCTIONS -----------------
-def generate_posts(hashtag: str, num_posts: int, use_gemini: bool):
+# ========== POST GENERATOR ==========
+def generate_posts(hashtag, n=20):
+    """Try Gemini first, fallback to HuggingFace generated posts"""
     posts = []
-    source = "huggingface"
-
-    if use_gemini and gemini_model:
-        prompt = f"Generate {num_posts} realistic social media posts about {hashtag}. Use emojis. Tone should vary (positive, negative, neutral)."
-        try:
-            response = gemini_model.generate_content(prompt)
-            text = response.text
-            posts = [line.strip("-• ") for line in text.split("\n") if line.strip()][:num_posts]
-            source = "gemini"
-        except Exception as e:
-            posts = [f"⚠️ Gemini API failed: {e}. Falling back to Hugging Face."]
-            source = "huggingface"
-
-    if not posts:  # fallback
-        template = [
-            f"Not sure how I feel about {hashtag} 🤔",
-            f"{hashtag} totally failed expectations 😠",
-            f"People are talking about {hashtag} everywhere 🌍",
-            f"{hashtag} campaign is the best thing this year 🎉",
+    source = "Gemini"
+
+    try:
+        prompt = f"Generate {n} realistic, diverse, short social media posts about {hashtag}. Use emojis."
+        response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
+        text = response.text.strip().split("\n")
+        posts = [t for t in text if len(t.strip()) > 0][:n]
+    except Exception:
+        source = "HuggingFace"
+        base_posts = [
+            f"{hashtag} is the best thing ever 🎉",
             f"I'm disappointed with {hashtag} 💔",
+            f"Not sure how I feel about {hashtag} 🤔",
             f"Super excited about {hashtag} 🔥",
+            f"People are talking about {hashtag} everywhere 🌍",
+            f"{hashtag} totally failed expectations 😠",
             f"I love {hashtag}! It's amazing ❤️",
         ]
-        posts = random.choices(template, k=num_posts)
+        posts = random.choices(base_posts, k=n)
 
     return posts, source
 
-
-def analyze_sentiments(hashtag, num_posts, visualization, use_gemini):
-    num_posts = min(num_posts, 50)  # cap at 50
-
-    posts, source = generate_posts(hashtag, num_posts, use_gemini)
-
-    results = []
-    for post in posts:
-        try:
-            result = sentiment_pipeline(post[:512])[0]
-            results.append((post, result["label"], round(result["score"], 2)))
-        except Exception:
-            results.append((post, "NEUTRAL", 0.5))
-
-    # Visualization
-    sentiments = [r[1] for r in results]
-    fig = None
-    if visualization == "Bar":
-        fig = px.bar(x=sentiments, title=f"Sentiment Distribution for {hashtag}", labels={"x": "Sentiment", "y": "Count"})
-    elif visualization == "Pie":
-        fig = px.pie(names=sentiments, title=f"Sentiment Share for {hashtag}")
-    elif visualization == "Line":
-        fig = px.line(y=[1 if s == "POSITIVE" else -1 if s == "NEGATIVE" else 0 for s in sentiments],
-                      title=f"Sentiment Rolling Trend for {hashtag}")
-
-    summary_text = f"✅ Posts generated by **{source.upper()}**\nTotal Posts: {len(results)}"
-    return results, fig, summary_text
-
-# ----------------- UI -----------------
-CUSTOM_CSS = """
-#col-left {
-  background: rgba(25, 25, 25, 0.85);
-  padding: 20px;
-  border-radius: 16px;
-  box-shadow: 0 0 20px rgba(0,0,0,0.4);
-  animation: fadeInLeft 1s ease;
-}
-#col-right {
-  background: rgba(15, 15, 15, 0.85);
-  padding: 20px;
-  border-radius: 16px;
-  box-shadow: 0 0 20px rgba(0,0,0,0.4);
-  animation: fadeInRight 1s ease;
-}
-.posts-table {
-  max-height: 420px;
-  overflow-y: auto;
-}
-@keyframes fadeInLeft {
-  from {opacity: 0; transform: translateX(-30px);}
-  to {opacity: 1; transform: translateX(0);}
-}
-@keyframes fadeInRight {
-  from {opacity: 0; transform: translateX(30px);}
-  to {opacity: 1; transform: translateX(0);}
-}
-button {
-  transition: all 0.2s ease;
-}
-button:hover {
-  transform: scale(1.05);
-  box-shadow: 0 0 12px #ff6600;
-}
-"""
-
-with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft()) as demo:
-    gr.HTML("<h1 style='text-align:center; color:#FF6600;'>🚀 Social Media Sentiment Analyzer</h1>")
-    gr.HTML("<p style='text-align:center;'>Stream posts • Analyze moods • Visualize trends</p>")
-
-    with gr.Row():
-        with gr.Column(elem_id="col-left"):
-            hashtag = gr.Textbox(label="Enter Hashtag", value="#gla")
-            num_posts = gr.Slider(5, 50, value=20, step=1, label="Number of Posts")
-            visualization = gr.Dropdown(choices=["Bar", "Pie", "Line"], value="Bar", label="Choose Visualization")
-            use_gemini = gr.Checkbox(label="Use Gemini Advanced Analysis", value=False)
-            run_btn = gr.Button("🔍 Run Analysis", variant="primary")
-
-        with gr.Column(elem_id="col-right"):
-            posts_table = gr.Dataframe(
-                headers=["Post", "Sentiment", "Confidence"],
-                interactive=False,
-                elem_classes=["posts-table"]
-            )
-            plot_out = gr.Plot()
-            summary = gr.Markdown()
-
-    run_btn.click(
-        analyze_sentiments,
-        inputs=[hashtag, num_posts, visualization, use_gemini],
-        outputs=[posts_table, plot_out, summary]
+# ========== SENTIMENT ANALYSIS ==========
+def analyze_posts(posts):
+    results = sentiment_pipeline(posts)
+    data = []
+    for post, res in zip(posts, results):
+        data.append({
+            "Post": post,
+            "Sentiment": res["label"],
+            "Confidence": round(res["score"], 2)
+        })
+    return pd.DataFrame(data)
+
+# ========== STREAMLIT UI ==========
+st.set_page_config(
+    page_title="AI Sentiment Analyzer",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+st.markdown(
+    """
+    <style>
+    body {
+        background: linear-gradient(135deg, #0f2027, #203a43, #2c5364);
+        color: white;
+    }
+    .stButton button {
+        background: linear-gradient(45deg, #ff6b6b, #f7b733);
+        color: white;
+        font-weight: bold;
+        border-radius: 12px;
+        padding: 10px 20px;
+        transition: 0.3s;
+    }
+    .stButton button:hover {
+        transform: scale(1.05);
+        background: linear-gradient(45deg, #36d1dc, #5b86e5);
+    }
+    .moving {
+        animation: float 3s ease-in-out infinite;
+    }
+    @keyframes float {
+        0% { transform: translatey(0px); }
+        50% { transform: translatey(-10px); }
+        100% { transform: translatey(0px); }
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.title("🚀 Social Media Sentiment Analyzer")
+st.markdown("### Stream posts • Analyze moods • Visualize trends with animations")
+
+# Sidebar
+st.sidebar.header("⚙️ Controls")
+hashtag = st.sidebar.text_input("Enter Hashtag", "#gla")
+num_posts = st.sidebar.slider("Number of Posts", 5, 50, 20)
+vis_type = st.sidebar.selectbox("Choose Visualization", ["Bar", "Pie", "Line"])
+
+if st.sidebar.button("🔍 Run Analysis"):
+    with st.spinner("Generating posts and analyzing sentiments..."):
+        posts, source = generate_posts(hashtag, num_posts)
+        df = analyze_posts(posts)
+
+    st.success(f"✅ Posts generated by **{source}** | Total: {len(posts)}")
+
+    st.dataframe(df, use_container_width=True)
+
+    # ===== VISUALIZATIONS =====
+    if vis_type == "Bar":
+        fig = px.bar(df, x="Sentiment", color="Sentiment", title=f"Sentiment Distribution for {hashtag}", animation_frame=None)
+    elif vis_type == "Pie":
+        fig = px.pie(df, names="Sentiment", title=f"Sentiment Share for {hashtag}", hole=0.4)
+    else:
+        fig = px.line(df, y="Confidence", title=f"Confidence Trend for {hashtag}")
+
+    st.plotly_chart(fig, use_container_width=True)
+
+    # ===== Moving 3D-like floating animation =====
+    st.markdown(
+        f"""
+        <div class="moving" style="font-size:24px; text-align:center; margin-top:30px;">
+        🌍 🔥 ❤️ 🎉 Trending Vibes around <b>{hashtag}</b>
+        </div>
+        """,
+        unsafe_allow_html=True
     )
-
-# ----------------- MAIN -----------------
-if __name__ == "__main__":
-    demo.launch()