Create app.py

app.py

@@ -0,0 +1,137 @@
+# prompt: conbine all the models above in one gradio interface , and well customized 
+
+import gradio as gr
+from transformers import pipeline
+import csv
+import heapq as hq
+from google.colab import files
+
+# Install necessary libraries
+!pip install transformers gradio librosa
+
+
+# Sentiment Analysis
+classifier_sentiment = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+def analyze_sentiment(text):
+    result = classifier_sentiment(text)[0]
+    label = result['label']
+    score = result['score']
+    return f"Label: {label},"
+
+# Translation
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
+def translate_text(text):
+    result = translator(text)[0]
+    translated_text = result["translation_text"]
+    return translated_text
+
+# Image Classification
+classifier_image = pipeline("image-classification", model="google/mobilenet_v2_1.0_224")
+def classify_image(image):
+  results = classifier_image(image)
+  output = ""
+  for result in results:
+      output += f"{result['label']}: {result['score']:.2f}\n"
+  return output
+
+# Speech to Text
+speech_to_text = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
+def transcribe_audio(audio):
+    text = speech_to_text(audio)["text"]
+    return text
+
+# Text Summarization
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+def summarize_text(text):
+  summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
+  return summary
+
+# Woodall Number
+def is_woodall(x):
+  if (x % 2 == 0): 
+    return False
+  if (x == 1): 
+    return True
+  x = x + 1 
+  p = 0
+  while (x % 2 == 0): 
+    x = x/2
+    p = p + 1
+    if (p == x): 
+      return True
+  return False
+
+
+# Largest Numbers from a List (Heap Queue)
+def heap_queue_largest(nums,n):
+    try:
+        largest_nums = hq.nlargest(n, nums)
+        return largest_nums
+    except Exception as e:
+      return f"Error: {e}"
+
+
+# CSV Reader
+def csv_reader(filepath):
+    try:
+        with open(filepath, 'r') as f:
+          reader = csv.reader(f)
+          data = [row for row in reader]
+          return data
+    except Exception as e:
+      return f"Error: {e}"
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1>Multi-functional AI Demo</h1>")
+
+    with gr.Tab("Sentiment Analysis"):
+        text_input = gr.Textbox(placeholder="Enter text here...")
+        text_output = gr.Textbox()
+        sentiment_button = gr.Button("Analyze")
+        sentiment_button.click(analyze_sentiment, inputs=text_input, outputs=text_output)
+
+    with gr.Tab("Translation"):
+      text_input_trans = gr.Textbox(placeholder="Enter English text here...")
+      text_output_trans = gr.Textbox()
+      trans_button = gr.Button("Translate")
+      trans_button.click(translate_text, inputs=text_input_trans, outputs=text_output_trans)
+
+    with gr.Tab("Image Classification"):
+        image_input = gr.Image(type="pil")
+        image_output = gr.Textbox()
+        image_button = gr.Button("Classify")
+        image_button.click(classify_image, inputs=image_input, outputs=image_output)
+
+    with gr.Tab("Speech to Text"):
+        audio_input = gr.Audio(sources=["microphone"], type="filepath")
+        audio_output = gr.Textbox()
+        audio_button = gr.Button("Transcribe")
+        audio_button.click(transcribe_audio, inputs=audio_input, outputs=audio_output)
+    
+    with gr.Tab("Text Summarization"):
+        text_input_summ = gr.Textbox(placeholder="Enter text here...")
+        text_output_summ = gr.Textbox()
+        summ_button = gr.Button("Summarize")
+        summ_button.click(summarize_text, inputs=text_input_summ, outputs=text_output_summ)
+
+    with gr.Tab("Woodall Number"):
+      woodall_input = gr.Number(label="Enter a number:")
+      woodall_output = gr.Textbox()
+      woodall_button = gr.Button("Check")
+      woodall_button.click(is_woodall, inputs=woodall_input, outputs=woodall_output)
+
+    with gr.Tab("Largest Numbers (Heap Queue)"):
+      nums_input = gr.Textbox(label="Enter numbers separated by spaces:")
+      n_input = gr.Number(label="Number of largest elements:")
+      nums_output = gr.Textbox()
+      nums_button = gr.Button("Find Largest")
+      nums_button.click(heap_queue_largest, inputs=[nums_input, n_input], outputs=nums_output)
+
+    with gr.Tab("CSV Reader"):
+      csv_input = gr.File(label="Upload CSV File")
+      csv_output = gr.Textbox()
+      csv_button = gr.Button("Read CSV")
+      csv_button.click(csv_reader, inputs=csv_input, outputs=csv_output)
+
+demo.launch()