Update app.py

app.py

@@ -1,17 +1,19 @@
 import gradio as gr
 from textblob import TextBlob
 from deepface import DeepFace
-import moviepy.editor as mp
-import cv2
 import tempfile
 import os
+import cv2
+import moviepy.editor as mp
 
+# Sentiment Analysis for Text
 def analyze_text(text):
     blob = TextBlob(text)
     polarity = blob.sentiment.polarity
     sentiment = "Positive" if polarity > 0 else "Negative" if polarity < 0 else "Neutral"
-    return f"Text Sentiment: {sentiment} (Polarity: {polarity:.2f}
-    
+    return f"Sentiment: {sentiment} (Polarity: {polarity:.2f})"
+
+# Emotion Analysis for Image (Face Recognition)
 def analyze_image(image):
     try:
         result = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
@@ -19,10 +21,12 @@ def analyze_image(image):
         return f"Detected Emotion: {dominant_emotion}"
     except Exception as e:
         return f"Error: {str(e)}"
-def analyze_video(video_file):
+
+# Emotion Analysis for Video (Face Recognition)
+def analyze_video(video):
     try:
         tmpdir = tempfile.mkdtemp()
-        clip = mp.VideoFileClip(video_file)
+        clip = mp.VideoFileClip(video)
         frame = clip.get_frame(clip.duration / 2)
         frame_path = os.path.join(tmpdir, "frame.jpg")
         cv2.imwrite(frame_path, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
@@ -32,24 +36,32 @@ def analyze_video(video_file):
     except Exception as e:
         return f"Error: {str(e)}"
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 Emotion and Sentiment Analyzer")
-    with gr.Tab("Text Analysis"):
-        text_input = gr.Textbox(label="Enter Text")
-        text_output = gr.Textbox(label="Sentiment Result")
-        text_btn = gr.Button("Analyze Text")
-        text_btn.click(analyze_text, inputs=text_input, outputs=text_output)
-
-    with gr.Tab("Image Analysis"):
-        img_input = gr.Image(type="filepath", label="Upload Face Image")
-        img_output = gr.Textbox(label="Emotion Result")
-        img_btn = gr.Button("Analyze Image")
-        img_btn.click(analyze_image, inputs=img_input, outputs=img_output)
-
-    with gr.Tab("Video Analysis"):
-        video_input = gr.Video(label="Upload Face Video")
-        video_output = gr.Textbox(label="Emotion Result")
-        video_btn = gr.Button("Analyze Video")
-        video_btn.click(analyze_video, inputs=video_input, outputs=video_output)
-
-demo.launch()
+# Gradio Blocks UI
+with gr.Blocks(theme="huggingface") as demo:
+    gr.Markdown("# 🎭 Sentiment & Emotion Decoder", elem_id="header")
+    gr.Markdown("Upload your text, face image, or video to decode emotions and sentiments!")
+
+    with gr.Tabs():
+        # Text Sentiment Analysis Tab
+        with gr.TabItem("📜 Text Sentiment"):
+            text_input = gr.Textbox(label="Enter Text Here", placeholder="Type your social media post here...")
+            text_button = gr.Button("🔍 Analyze Sentiment")
+            text_output = gr.Label(label="Sentiment Result")
+            text_button.click(analyze_text, inputs=text_input, outputs=text_output)
+
+        # Image Emotion Analysis Tab
+        with gr.TabItem("📸 Face Emotion Image"):
+            img_input = gr.Image(type="filepath", label="Upload Face Image")
+            img_output = gr.Label(label="Emotion Result")
+            img_button = gr.Button("🔍 Analyze Image")
+            img_button.click(analyze_image, inputs=img_input, outputs=img_output)
+
+        # Video Emotion Analysis Tab
+        with gr.TabItem("🎥 Face Emotion Video"):
+            video_input = gr.Video(label="Upload Face Video")
+            video_output = gr.Label(label="Emotion Result")
+            video_button = gr.Button("🔍 Analyze Video")
+            video_button.click(analyze_video, inputs=video_input, outputs=video_output)
+
+# Launch the Interface
+demo.launch()