Update app.py

app.py

@@ -1,17 +1,77 @@
+# In Google Colab
+
+app_code = '''
 import gradio as gr
+from transformers import pipeline
+from deepface import DeepFace
+import cv2
+import numpy as np
+import tempfile
+import moviepy.editor as mp
+
+# Load Text Sentiment Model
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+# 1. Text Sentiment Analysis
+def analyze_text(text):
+    result = sentiment_pipeline(text)[0]
+    return f"{result['label']} ({result['score']*100:.2f}%)"
+
+# 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 middle frame
+    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("# 🎯 Deep Learning Sentiment & Emotion Analyzer")
+    gr.Markdown("Analyze **Text**, **Face Image**, or **Video**!")
 
-from transforme import pipeline
+    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)
 
-sentiment = pipeline("emoition-analysis")
+        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)
 
-def get_emotion( input_text):
-    return emotion(input_text)
+        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)
 
-iface =  gr.Inface(in = get_emotion,
-                  input = "text",
-                  outputs = ["text"],
-                  title = 'emotion analysis',
-                  description='get emotion negative/positive for the given input')
+demo.launch()
+'''
 
-iface.lanch(inline = false)
+# Save to app.py
+with open("app.py", "w") as f:
+    f.write(app_code)
 
+print("✅ app.py saved!")