Create app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr #importing the necessary libraries
+from transformers import pipeline
+
+sentiment_pipeline = pipeline("sentiment-analysis") #importing the model that we will use to work at, as explained in the output terminal
+chatbot_pipeline = pipeline("text-generation")
+summarization_pipeline = pipeline("summarization", model="t5-small")  # T5 for summarization
+
+# Sentiment Analysis function 
+def analyze_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    return result["label"], round(result["score"], 2)
+
+def generate_response(user_input):
+    response = chatbot_pipeline(user_input, max_length=100, num_return_sequences=1)
+    return response[0]['generated_text']
+
+# Summarization function
+def summarize_text(long_text):
+    summary = summarization_pipeline(long_text, max_length=50, min_length=25, do_sample=False)
+    return summary[0]["summary_text"]
+
+# Speech-to-Text function
+def text_to_speech(text):
+    from gtts import gTTS
+    import os
+    tts = gTTS(text)
+    tts.save("output.mp3")
+    return "output.mp3"
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Multi-Task AI App with Alternative Models")
+    with gr.Tabs():
+        # tab for Sentiment Analysis 
+        with gr.Tab("Sentiment Analysis"):
+            gr.Markdown("### Sentiment Analysis")
+            sentiment_input = gr.Textbox(label="Enter Text for Sentiment Analysis")
+            sentiment_output_label = gr.Textbox(label="Sentiment")
+            sentiment_output_score = gr.Textbox(label="Confidence Score")
+            gr.Button("Analyze").click(analyze_sentiment, inputs=sentiment_input, outputs=[sentiment_output_label, sentiment_output_score])
+        
+        # tab for chatbot
+        with gr.Tab("Chatbot"):
+            gr.Markdown("### Chatbot")
+            chatbot_input = gr.Textbox(label="Enter Your Message")
+            chatbot_output = gr.Textbox(label="Chatbot Response", lines=5)
+            gr.Button("Send").click(chatbot_response, inputs=chatbot_input, outputs=chatbot_output)
+        
+        # tab for summarization
+        with gr.Tab("Summarization"):
+            gr.Markdown("### Summarization")
+            summarization_input = gr.Textbox(label="Enter Long Text for Summarization", lines=5)
+            summarization_output = gr.Textbox(label="Summary", lines=5)
+            gr.Button("Summarize").click(summarize_text, inputs=summarization_input, outputs=summarization_output)
+        
+        # tab for STT
+        with gr.Tab("Speech-to-Text"):
+            gr.Markdown("### Text-to-Speech")
+            tts_input = gr.Textbox(label="Enter Text for Text-to-Speech")
+            tts_output = gr.Audio(label="Generated Speech")
+            gr.Button("Convert to Speech").click(text_to_speech, inputs=tts_input, outputs=tts_output)
+
+
+demo.launch()
+
+
+
+