Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled4.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1352Z_3Tsa5_YFTfI-jWhZpSJ_k4yHSm3
+"""
+
+pip install gradio transformers torch
+
+!pip install gTTS
+
+import gradio as gr
+from transformers import pipeline, TextGenerationPipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
+import torch
+from gtts import gTTS
+import tempfile
+
+sentiment_pipeline = pipeline("sentiment-analysis")
+summarizer_pipeline = pipeline("summarization")
+
+
+# Sentiment Analysis
+def analyze_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    return result["label"], round(result["score"], 3)
+
+
+# Summarization
+def summarize(text):
+    summary = summarizer_pipeline(text, max_length=60, min_length=15, do_sample=False)
+    return summary[0]["summary_text"]
+
+
+def text_to_speech(text):
+    tts = gTTS(text)
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
+        tts.save(fp.name)
+        return fp.name
+
+
+# Gradio UI
+with gr.Blocks(title="TrailTrek AI Assistant",theme="soft") as demo:
+    gr.Markdown("## 🧠 TrailTrek Gears Co - Multi-Task AI Demo")
+
+    with gr.Tab("📊 Sentiment Analysis"):
+        with gr.Row():
+            text_input = gr.Textbox(label="Enter text")
+            sentiment_output = gr.Text(label="Sentiment")
+            confidence_output = gr.Number(label="Confidence")
+        analyze_btn = gr.Button("Analyze")
+        analyze_btn.click(analyze_sentiment, inputs=[text_input], outputs=[sentiment_output, confidence_output])
+
+
+    with gr.Tab("📄 Summarization"):
+        input_text = gr.Textbox(lines=8, label="Enter a long text")
+        output_summary = gr.Text(label="Summary")
+        summarize_btn = gr.Button("Summarize")
+        summarize_btn.click(summarize, inputs=[input_text], outputs=[output_summary])
+
+
+    with gr.Tab("🗣️ Text-to-Speech"):
+        tts_input = gr.Textbox(label="Enter text to speak")
+        tts_output = gr.Audio(label="Generated Speech", type="filepath")
+        tts_btn = gr.Button("Convert to Speech")
+        tts_btn.click(text_to_speech, inputs=[tts_input], outputs=[tts_output])
+
+
+
+demo.launch()
+