Create app.py

app.py

@@ -1,71 +1,14 @@
+# In Colab
+code = '''
 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
+def greet(name):
+    return "Hello " + name + "!"
 
-# 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)}"
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()
+'''
 
-# 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()
+# Save it as app.py
+with open("app.py", "w") as f:
+    f.write(code)