Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import random
+
+# Load sentiment analysis model
+sentiment_pipe = pipeline("sentiment-analysis")
+
+# Load summarization model
+summarizer = pipeline("summarization")
+
+# Load text-to-speech model
+tts_pipe = pipeline("text-to-speech", model="suno/bark-small")
+
+## real work now
+
+# Sentiment Analysis Function
+def get_sentiment(input_text):
+    analysis = sentiment_pipe(input_text)[0]
+    return analysis['label'], str(round(analysis['score'], 4))
+
+# Summarization Function
+def summarize_text(input_text):
+    summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)[0]
+    return summary['summary_text']
+
+# Text-to-Speech Function
+def text_to_speech(input_text):
+    speech = tts_pipe(input_text)
+    return speech["path"]
+
+# Chatbot Logic
+def chat(message, history):
+    history = history or []
+    if message.startswith("How many"):
+        response = str(random.randint(1, 10))
+    elif message.startswith("How"):
+        response = random.choice(["Great", "Good", "Okay", "Bad"])
+    elif message.startswith("Where"):
+        response = random.choice(["Here", "There", "Somewhere"])
+    else:
+        response = "I don't know"
+    history.append((message, response))
+    return history, history
+
+# Create Gradio Interface
+with gr.Blocks(title="TrailTrek AI Suite") as demo:
+    gr.Markdown("# TrailTrek Gears Co. AI Prototype")
+
+    with gr.Tabs():
+        # Sentiment Analysis Tab
+        with gr.Tab("Sentiment Analysis"):
+            gr.Markdown("## Analyze Text Sentiment")
+            with gr.Row():
+                text_input = gr.Textbox(label="Input Text")
+                with gr.Column():
+                    sentiment_label = gr.Textbox(label="Sentiment")
+                    score_output = gr.Textbox(label="Confidence Score")
+            analyze_btn = gr.Button("Analyze")
+            analyze_btn.click(
+                fn=get_sentiment,
+                inputs=text_input,
+                outputs=[sentiment_label, score_output]
+            )
+
+        # Chatbot Tab
+        with gr.Tab("Chatbot"):
+            gr.Markdown("## Interactive Chat")
+            chatbot = gr.Chatbot()
+            msg = gr.Textbox(label="Your Message")
+            clear = gr.Button("Clear")
+            msg.submit(chat, [msg, chatbot], [chatbot, msg])
+            clear.click(lambda: None, None, chatbot, queue=False)
+
+        # Summarization Tab
+        with gr.Tab("Summarization"):
+            gr.Markdown("## Text Summarization")
+            with gr.Row():
+                long_text = gr.Textbox(label="Input Text", lines=5)
+                summary = gr.Textbox(label="Summary", lines=5)
+            summarize_btn = gr.Button("Summarize")
+            summarize_btn.click(
+                fn=summarize_text,
+                inputs=long_text,
+                outputs=summary
+            )
+
+        # Text-to-Speech Tab
+        with gr.Tab("Text-to-Speech"):
+            gr.Markdown("## Web Accessibility Prototype")
+            with gr.Row():
+                tts_input = gr.Textbox(label="Enter Text")
+                tts_output = gr.Audio(label="Generated Speech")
+            tts_btn = gr.Button("Convert to Speech")
+            tts_btn.click(
+                fn=text_to_speech,
+                inputs=tts_input,
+                outputs=tts_output
+            )
+
+
+# Launch the Gradio app
+demo.launch()