Update app.py

app.py

@@ -1,120 +1,163 @@
 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
+import pandas as pd
+from transformers import pipeline
+import google.generativeai as genai
 
-# ---------------------------
-# Load Hugging Face sentiment model
-# ---------------------------
+# ------------------------------
+# Setup HuggingFace Transformer
+# ------------------------------
 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)
+# ------------------------------
+# Setup Gemini (Google AI Studio)
+# ------------------------------
+GEMINI_KEY = "YOUR_GEMINI_API_KEY"
+genai.configure(api_key=GEMINI_KEY)
+
+# Helper to call Gemini
+def gemini_generate(prompt):
+    model = genai.GenerativeModel("gemini-1.5-flash")
+    response = model.generate_content(prompt)
+    return response.text
+
+# ------------------------------
+# Fake post generator with Gemini
+# ------------------------------
+def generate_posts(hashtag, n_posts, use_gemini=False):
+    posts = []
+
+    if use_gemini:
+        prompt = f"""
+        Generate {n_posts} short social media posts about "{hashtag}".
+        Posts must be realistic, 1-2 sentences each, like from Twitter.
+        Ensure balanced sentiment: mostly realistic negatives if topic is bad,
+        some neutral, and very few positive.
+        Return only the posts as a numbered list.
+        """
+        try:
+            text = gemini_generate(prompt)
+            posts = [line.split(". ",1)[-1] for line in text.split("\n") if line.strip()]
+        except Exception as e:
+            posts = [f"Error generating with Gemini: {e}"]
+    else:
+        sample_posts = [
+            f"{hashtag} totally failed expectations 😞",
+            f"Not sure how I feel about {hashtag} 🤔",
+            f"{hashtag} is the worst thing ever 😡",
+            f"Super excited about {hashtag} 🔥",
+            f"People are talking about {hashtag} everywhere 🌍",
+            f"{hashtag} campaign is the best thing this year 🎉",
+            f"I'm disappointed with {hashtag} 💔",
+            f"I love {hashtag}! It's amazing ❤️"
+        ]
+        posts = random.choices(sample_posts, k=n_posts)
+
+    return posts[:n_posts]
+
+# ------------------------------
+# Sentiment Analysis
+# ------------------------------
+def analyze_sentiment(posts, use_gemini=False):
     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>
+    if use_gemini:
+        # Limit Gemini analysis to 10 posts (quota safe)
+        subset = posts[:10]
+        prompt = f"""
+        Analyze the sentiment of the following posts:
+        {subset}
+        Respond as JSON list with: post, sentiment (POSITIVE/NEGATIVE/NEUTRAL), confidence (0-1).
         """
-    )
+        try:
+            text = gemini_generate(prompt)
+            # naive parse: fallback to HF if parsing fails
+            if "POSITIVE" in text or "NEGATIVE" in text:
+                for line in text.split("\n"):
+                    if line.strip():
+                        if "POSITIVE" in line:
+                            results.append({"post": line, "sentiment": "POSITIVE", "confidence": 1.0})
+                        elif "NEGATIVE" in line:
+                            results.append({"post": line, "sentiment": "NEGATIVE", "confidence": 1.0})
+                        elif "NEUTRAL" in line:
+                            results.append({"post": line, "sentiment": "NEUTRAL", "confidence": 1.0})
+            else:
+                raise Exception("Parsing failed")
+        except:
+            # fallback to HF
+            results = [{"post": p, **sentiment_model(p)[0]} for p in posts]
+
+    else:
+        results = [{"post": p, **sentiment_model(p)[0]} for p in posts]
+
+    # Format uniform
+    clean = []
+    for r in results:
+        label = r["label"] if "label" in r else r["sentiment"]
+        score = r["score"] if "score" in r else r.get("confidence", 0.8)
+        clean.append({
+            "Post": r["post"],
+            "Sentiment": label.upper(),
+            "Confidence": round(score, 2)
+        })
+    return clean
+
+# ------------------------------
+# Visualization
+# ------------------------------
+def create_viz(data, viz_type, hashtag):
+    df = pd.DataFrame(data)
+
+    if viz_type == "Bar":
+        fig = px.bar(df, x="Sentiment", title=f"Sentiment Distribution for {hashtag}")
+    elif viz_type == "Pie":
+        fig = px.pie(df, names="Sentiment", title=f"Sentiment Share for {hashtag}")
+    elif viz_type == "Line":
+        fig = px.line(df, y="Confidence", title=f"Sentiment Confidence Trend for {hashtag}")
+    elif viz_type == "Area":
+        fig = px.area(df, y="Confidence", title=f"Sentiment Rolling Area for {hashtag}")
+    else:
+        fig = px.histogram(df, x="Sentiment", title=f"Sentiment Histogram for {hashtag}")
+
+    return fig
+
+# ------------------------------
+# Main App Function
+# ------------------------------
+def run_analysis(hashtag, n_posts, viz_type, use_gemini):
+    posts = generate_posts(hashtag, n_posts, use_gemini)
+    data = analyze_sentiment(posts, use_gemini)
+    fig = create_viz(data, viz_type, hashtag)
+    return pd.DataFrame(data), fig
+
+# ------------------------------
+# Gradio UI
+# ------------------------------
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="purple")) as demo:
+    gr.HTML("""
+    <div style="text-align:center; padding:20px; color:white;">
+        <h1 style="font-size:40px; background: linear-gradient(90deg, #ff8c00, #e94057, #8a2be2);
+        -webkit-background-clip: text; -webkit-text-fill-color: transparent;">
+        🚀 Social Media Sentiment Analyzer</h1>
+        <p style="font-size:18px;">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")
+            hashtag = gr.Textbox(label="Enter Hashtag", placeholder="#gla")
+            n_posts = gr.Slider(5, 50, step=1, value=20, label="Number of Posts")
+            viz_type = gr.Dropdown(["Bar", "Pie", "Line", "Area"], value="Bar", label="Choose Visualization")
+            use_gemini = gr.Checkbox(label="Use Gemini Advanced Analysis")
+            run_btn = gr.Button("🚀 Run Analysis", variant="primary")
 
         with gr.Column(scale=2):
-            output_table = gr.Dataframe(label="Posts & Sentiments", wrap=True)
+            output_table = gr.Dataframe(headers=["Post", "Sentiment", "Confidence"], label="Posts & Sentiments")
             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>")
+    run_btn.click(fn=run_analysis, 
+                  inputs=[hashtag, n_posts, viz_type, use_gemini], 
+                  outputs=[output_table, output_plot])
 
 demo.launch()