Update app.py

app.py

@@ -1,64 +1,235 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import pipeline
+from datetime import datetime
+import matplotlib.pyplot as plt
+import io
+import base64
+from langdetect import detect
+import qrcode
+from wordcloud import WordCloud
+import nltk
+from nltk.tokenize import sent_tokenize
+from better_profanity import profanity
+import tempfile
+import os
+
+# Download NLTK data
+nltk.download('punkt', quiet=True)
+
+# Model options
+models = {
+    "DistilBERT": "distilbert-base-uncased-finetuned-sst-2-english",
+    "Twitter RoBERTa": "cardiffnlp/twitter-roberta-base-sentiment-latest"
+}
+analyzer = None
+history = []
+sentiment_scores = []
+feedback_log = []
+
+# Load the selected or custom model
+def load_model(model_name, custom_model_path=None):
+    global analyzer
+    if custom_model_path:
+        analyzer = pipeline("sentiment-analysis", model=custom_model_path)
+        return f"Loaded custom model from {custom_model_path}"
+    analyzer = pipeline("sentiment-analysis", model=models[model_name])
+    return f"Loaded {model_name} model."
+
+# Highlight words
+def highlight_words(text, sentiment, pos_words, neg_words):
+    words = text.split()
+    highlighted = []
+    pos_list = pos_words.split(",") if pos_words else ["love", "great", "happy", "awesome", "good"]
+    neg_list = neg_words.split(",") if neg_words else ["hate", "bad", "terrible", "awful", "sad"]
+    for word in words:
+        if sentiment == "POSITIVE" and word.lower() in [w.strip().lower() for w in pos_list]:
+            highlighted.append(f"**{word}**")
+        elif sentiment == "NEGATIVE" and word.lower() in [w.strip().lower() for w in neg_list]:
+            highlighted.append(f"**{word}**")
+        else:
+            highlighted.append(word)
+    return " ".join(highlighted)
+
+# Sentiment analysis with context
+def analyze_sentiment(text, model_name, pos_words, neg_words, intensity, custom_model_path=None, source="manual", compare_text=None):
+    if not text or not text.strip():
+        return "Error: Please enter some text.", "", "", None, None, "", None, ""
+    try:
+        if analyzer is None:
+            load_model(model_name, custom_model_path)
+        
+        # Language and profanity check
+        lang = detect(text)
+        lang_note = " (Warning: Text may not be in English)" if lang != "en" else ""
+        profanity_note = " (Warning: Inappropriate language detected)" if profanity.contains_profanity(text) else ""
+        
+        # Contextual analysis
+        sentences = sent_tokenize(text)
+        results = []
+        for sent in sentences:
+            result = analyzer(sent)[0]
+            label, score = result['label'], result['score']
+            if score < intensity:
+                label = "NEUTRAL"
+            results.append(f"{sent} -> {label} ({score:.2f})")
+        combined_result = analyzer(text)[0]
+        label, score = combined_result['label'], combined_result['score']
+        if score < intensity:
+            label, emoji = "NEUTRAL", "😐"
+        else:
+            emoji = "😊" if "POSITIVE" in label.upper() else "😞" if "NEGATIVE" in label.upper() else "😐"
+        confidence_note = " (Low confidence)" if score < 0.7 else ""
+        sentiment_result = f"Overall: {label} {emoji} (Confidence: {score:.2f}{confidence_note}{lang_note}{profanity_note})\n" + "\n".join(results)
+        highlighted_text = highlight_words(text, label, pos_words, neg_words)
+        
+        # History and scores
+        timestamp = datetime.now().strftime('%H:%M:%S')
+        history.append(f"[{timestamp}] {source}: {text} -> {sentiment_result.splitlines()[0]}")
+        sentiment_scores.append((timestamp, 1 if "POSITIVE" in label.upper() else -1 if "NEGATIVE" in label.upper() else 0))
+        history_str = "\n".join([h for h in history[-5:]])
+        
+        # Visuals
+        trend_img = generate_timeline()
+        wordcloud_img = generate_wordcloud(text)
+        qr_img = generate_qr(f"https://example.com/share?text={text}&result={sentiment_result.splitlines()[0].replace(' ', '+')}")
+        
+        # Comparative analysis
+        compare_result = ""
+        if compare_text:
+            comp_result = analyzer(compare_text)[0]
+            comp_label, comp_score = comp_result['label'], comp_result['score']
+            comp_emoji = "😊" if "POSITIVE" in comp_label.upper() else "😞" if "NEGATIVE" in comp_label.upper() else "😐"
+            compare_result = f"Comparison: {comp_label} {comp_emoji} (Confidence: {comp_score:.2f})"
+        
+        return sentiment_result, highlighted_text, history_str, trend_img, wordcloud_img, qr_img, compare_result, ""
+    except Exception as e:
+        return f"Error: {str(e)}", "", "", None, None, "", "", ""
+
+# Fetch X post (simulated)
+def fetch_x_post(x_url, model_name, pos_words, neg_words, intensity, custom_model_path):
+    sample_text = "Sample X post from " + x_url
+    return analyze_sentiment(sample_text, model_name, pos_words, neg_words, intensity, custom_model_path, source="X post")
+
+# Generate timeline
+def generate_timeline():
+    if not sentiment_scores:
+        return None
+    times, scores = zip(*sentiment_scores[-10:])
+    plt.figure(figsize=(6, 3))
+    plt.plot(times, scores, marker='o', linestyle='-', color='b')
+    plt.title("Sentiment Timeline")
+    plt.xlabel("Time")
+    plt.ylabel("Sentiment")
+    plt.ylim(-1.5, 1.5)
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    img_str = "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
+    plt.close()
+    return img_str
+
+# Generate word cloud
+def generate_wordcloud(text):
+    wordcloud = WordCloud(width=400, height=200, background_color="white").generate(text)
+    buf = io.BytesIO()
+    wordcloud.to_image().save(buf, format="PNG")
+    buf.seek(0)
+    img_str = "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
+    return img_str
+
+# Generate QR code
+def generate_qr(url):
+    qr = qrcode.QRCode(version=1, box_size=10, border=4)
+    qr.add_data(url)
+    qr.make(fit=True)
+    img = qr.make_image(fill="black", back_color="white")
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    buf.seek(0)
+    img_str = "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
+    return img_str
+
+# Export history with proper file handling
+def export_history():
+    if not history:
+        return None
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode="w") as temp_file:
+        temp_file.write("\n".join(history))
+        temp_path = temp_file.name
+    return temp_path
+
+# Log feedback
+def log_feedback(rating):
+    feedback_log.append(f"[{datetime.now().strftime('%H:%M:%S')}] Rating: {rating}/5")
+    return f"Feedback received! ({len(feedback_log)} total)"
+
+# Theme toggle function
+def toggle_theme(light_mode):
+    return "Theme switched to " + ("Light" if light_mode else "Dark") + ". Please refresh the page to apply."
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Monochrome()) as interface:
+    gr.Markdown("# Sentiment Analysis App")
+    gr.Markdown("Next-level sentiment analysis with context, comparison, and more!")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select Model", value="DistilBERT")
+            custom_model = gr.File(label="Upload Custom Model (optional)", file_types=[".bin", ".pt"])
+            text_input = gr.Textbox(label="Enter text or X URL", placeholder="Type text or paste an X URL...")
+            compare_input = gr.Textbox(label="Compare with (optional)", placeholder="Enter second text...")
+            audio_input = gr.Audio(label="Or Speak Your Text", type="filepath")
+            pos_words = gr.Textbox(label="Custom Positive Words", placeholder="love, great")
+            neg_words = gr.Textbox(label="Custom Negative Words", placeholder="hate, bad")
+            intensity_slider = gr.Slider(0.5, 1.0, value=0.7, label="Sentiment Intensity Threshold")
+            x_button = gr.Button("Analyze X Post")
+        with gr.Column(scale=3):
+            sentiment_output = gr.Textbox(label="Sentiment Result (Contextual)")
+            highlighted_output = gr.Textbox(label="Highlighted Text")
+            history_output = gr.Textbox(label="Analysis History (Last 5)", lines=5)
+            trend_output = gr.Image(label="Sentiment Timeline")
+            wordcloud_output = gr.Image(label="Word Cloud")
+            qr_output = gr.Image(label="Shareable QR Code")
+            compare_output = gr.Textbox(label="Comparative Analysis")
+
+    with gr.Row():
+        export_button = gr.Button("Export History")
+        export_file = gr.File(label="Download History")
+        theme_toggle = gr.Checkbox(label="Light Mode", value=False)
+        theme_status = gr.Textbox(label="Theme Status", value="Dark (default)")
+        feedback_slider = gr.Slider(1, 5, step=1, label="Rate this analysis (1-5)")
+        feedback_output = gr.Textbox(label="Feedback Status")
+
+    gr.Examples(
+        examples=["I love this app! It’s great.", "This is awful and sad.", "https://x.com/sample/post"],
+        inputs=[text_input]
+    )
+
+    # Event handlers
+    def audio_to_text(audio_file, model_name, pos_words, neg_words, intensity, custom_model_path):
+        text = "Simulated speech: I feel great today" if audio_file else ""
+        return analyze_sentiment(text, model_name, pos_words, neg_words, intensity, custom_model_path, source="audio")
+
+    text_input.change(
+        fn=analyze_sentiment,
+        inputs=[text_input, model_dropdown, pos_words, neg_words, intensity_slider, custom_model],
+        outputs=[sentiment_output, highlighted_output, history_output, trend_output, wordcloud_output, qr_output, compare_output, feedback_output]
+    )
+    x_button.click(
+        fn=fetch_x_post,
+        inputs=[text_input, model_dropdown, pos_words, neg_words, intensity_slider, custom_model],
+        outputs=[sentiment_output, highlighted_output, history_output, trend_output, wordcloud_output, qr_output, compare_output, feedback_output]
+    )
+    audio_input.change(
+        fn=audio_to_text,
+        inputs=[audio_input, model_dropdown, pos_words, neg_words, intensity_slider, custom_model],
+        outputs=[sentiment_output, highlighted_output, history_output, trend_output, wordcloud_output, qr_output, compare_output, feedback_output]
+    )
+    export_button.click(fn=export_history, inputs=None, outputs=export_file)
+    theme_toggle.change(fn=toggle_theme, inputs=theme_toggle, outputs=theme_status)
+    feedback_slider.change(fn=log_feedback, inputs=feedback_slider, outputs=feedback_output)
+
+# Launch the app
+interface.launch()