Create app.py

app.py

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+import gradio as gr
+from textblob import TextBlob
+import cv2
+import numpy as np
+from deepface import DeepFace
+import tempfile
+import moviepy.editor as mp
+
+# 1. Text Sentiment Analysis
+def analyze_text(text):
+    blob = TextBlob(text)
+    sentiment = blob.sentiment.polarity
+    if sentiment > 0:
+        result = "Positive Sentiment 😊"
+    elif sentiment < 0:
+        result = "Negative Sentiment 😢"
+    else:
+        result = "Neutral Sentiment 😐"
+    return result
+
+# 2. Face Emotion Detection
+def analyze_face(image):
+    try:
+        analysis = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
+        emotion = analysis[0]['dominant_emotion']
+        return f"Detected Emotion: {emotion}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# 3. Video Emotion Detection
+def analyze_video(video_file):
+    temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
+    with open(temp_video_path, "wb") as f:
+        f.write(video_file.read())
+
+    clip = mp.VideoFileClip(temp_video_path)
+    frame = clip.get_frame(clip.duration / 2)  # take frame at middle
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    try:
+        analysis = DeepFace.analyze(frame_rgb, actions=['emotion'], enforce_detection=False)
+        emotion = analysis[0]['dominant_emotion']
+        return f"Detected Emotion in Video: {emotion}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Emotion & Sentiment Analyzer 🎯")
+    gr.Markdown("Analyze Text, Face (Image), or Video for emotions!")
+
+    with gr.Tabs():
+        with gr.TabItem("Text Sentiment"):
+            text_input = gr.Textbox(label="Enter Text")
+            text_output = gr.Label()
+            text_button = gr.Button("Analyze Text")
+            text_button.click(analyze_text, inputs=text_input, outputs=text_output)
+
+        with gr.TabItem("Face Emotion (Image)"):
+            image_input = gr.Image(type="numpy", label="Upload Face Image")
+            image_output = gr.Label()
+            image_button = gr.Button("Analyze Face Emotion")
+            image_button.click(analyze_face, inputs=image_input, outputs=image_output)
+
+        with gr.TabItem("Video Emotion"):
+            video_input = gr.File(label="Upload Video (.mp4)")
+            video_output = gr.Label()
+            video_button = gr.Button("Analyze Video Emotion")
+            video_button.click(analyze_video, inputs=video_input, outputs=video_output)
+
+demo.launch()