Update app.py

app.py

@@ -1,163 +1,143 @@
-import gradio as gr
+import os
 import random
+import gradio as gr
 import plotly.express as px
 import pandas as pd
 from transformers import pipeline
 import google.generativeai as genai
 
-# ------------------------------
-# Setup HuggingFace Transformer
-# ------------------------------
+# -----------------------------
+# Load Hugging Face Sentiment Model
+# -----------------------------
 sentiment_model = pipeline("sentiment-analysis")
 
-# ------------------------------
-# 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 = []
-
-    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).
-        """
+# -----------------------------
+# Configure Gemini (Key from Secrets)
+# -----------------------------
+GEMINI_KEY = os.getenv("GEMINI_API_KEY")  # add in Hugging Face Secrets
+if GEMINI_KEY:
+    genai.configure(api_key=GEMINI_KEY)
+
+# -----------------------------
+# Fake Post Generator (simulate Twitter)
+# -----------------------------
+def generate_fake_posts(hashtag, n=20):
+    templates = [
+        f"I love {hashtag}! It's amazing ❤️",
+        f"I'm disappointed with {hashtag} 💔",
+        f"{hashtag} totally failed expectations 😠",
+        f"Not sure how I feel about {hashtag} 🤔",
+        f"People are talking about {hashtag} everywhere 🌍",
+        f"{hashtag} campaign is the best thing this year 🎉",
+        f"Super excited about {hashtag} 🔥",
+        f"{hashtag} is the worst thing ever 😡"
+    ]
+    return random.choices(templates, k=n)
+
+# -----------------------------
+# Run HuggingFace Analysis
+# -----------------------------
+def analyze_with_hf(posts):
+    results = sentiment_model(posts)
+    sentiments = []
+    for post, res in zip(posts, results):
+        sentiments.append({
+            "Post": post,
+            "Sentiment": res["label"],
+            "Confidence": round(res["score"], 2)
+        })
+    return sentiments
+
+# -----------------------------
+# Run Gemini Advanced Analysis
+# -----------------------------
+def analyze_with_gemini(posts):
+    if not GEMINI_KEY:
+        return analyze_with_hf(posts)  # fallback
+    
+    sentiments = []
+    for post in posts:
         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})
+            prompt = f"""Classify the sentiment of this social media post as Positive, Negative, or Neutral. 
+            Return only JSON with 'label' and 'confidence' between 0 and 1.
+
+            Post: "{post}"
+            """
+            response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
+            
+            # crude parse
+            text = response.text.strip()
+            if "Positive" in text:
+                label = "POSITIVE"
+            elif "Negative" in text:
+                label = "NEGATIVE"
             else:
-                raise Exception("Parsing failed")
-        except:
-            # fallback to HF
-            results = [{"post": p, **sentiment_model(p)[0]} for p in posts]
+                label = "NEUTRAL"
 
+            sentiments.append({
+                "Post": post,
+                "Sentiment": label,
+                "Confidence": 0.95  # Gemini usually doesn’t return prob, we keep 0.95
+            })
+        except:
+            sentiments.append({
+                "Post": post,
+                "Sentiment": "NEUTRAL",
+                "Confidence": 0.5
+            })
+    return sentiments
+
+# -----------------------------
+# Main Function
+# -----------------------------
+def run_analysis(hashtag, n_posts, vis_type, use_gemini):
+    posts = generate_fake_posts(hashtag, n_posts)
+    if use_gemini:
+        data = analyze_with_gemini(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):
+        data = analyze_with_hf(posts)
+    
     df = pd.DataFrame(data)
 
-    if viz_type == "Bar":
+    # Chart
+    if vis_type == "Bar":
         fig = px.bar(df, x="Sentiment", title=f"Sentiment Distribution for {hashtag}")
-    elif viz_type == "Pie":
+    elif vis_type == "Line":
+        fig = px.line(df, y="Confidence", title=f"Sentiment Rolling Trend for {hashtag}")
+    elif vis_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
+        fig = px.scatter(df, x="Sentiment", y="Confidence", title=f"Scatter of Sentiments for {hashtag}")
 
-# ------------------------------
-# 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
+    return df, 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.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="blue")) as demo:
+    gr.Markdown("<h1 style='text-align:center;'>🚀 Social Media Sentiment Analyzer</h1>")
+    gr.Markdown("<p style='text-align:center;'>Stream posts · Analyze moods · Visualize trends</p>")
 
     with gr.Row():
         with gr.Column(scale=1):
-            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")
-
+            hashtag = gr.Textbox(label="Enter Hashtag", value="#gla")
+            n_posts = gr.Slider(5, 50, value=20, step=1, label="Number of Posts")
+            vis_type = gr.Dropdown(["Bar", "Line", "Pie", "Scatter"], label="Choose Visualization", value="Bar")
+            use_gemini = gr.Checkbox(label="Use Gemini Advanced Analysis", value=False)
+            run_btn = gr.Button("🔍 Run Analysis", variant="primary")
+        
         with gr.Column(scale=2):
-            output_table = gr.Dataframe(headers=["Post", "Sentiment", "Confidence"], label="Posts & Sentiments")
-            output_plot = gr.Plot(label="Visualization")
-
-    run_btn.click(fn=run_analysis, 
-                  inputs=[hashtag, n_posts, viz_type, use_gemini], 
-                  outputs=[output_table, output_plot])
-
-demo.launch()
+            output_table = gr.Dataframe(headers=["Post", "Sentiment", "Confidence"], wrap=True)
+            output_plot = gr.Plot()
+
+    run_btn.click(
+        fn=run_analysis,
+        inputs=[hashtag, n_posts, vis_type, use_gemini],
+        outputs=[output_table, output_plot]
+    )
+
+# -----------------------------
+# Launch App
+# -----------------------------
+if __name__ == "__main__":
+    demo.launch()