Update app.py

app.py

@@ -1,13 +1,12 @@
+import tensorflow
+import torch
 import gradio as gr
-from transformers import pipeline
-from transformers import AutoModelForCausalLM
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
-
-
-# Load the NLP pipeline for text classification
+# --- Load the NLP pipeline for text classification ---
 classifier = pipeline("text-classification")
 
-# Define the function to generate mini-apps based on user input
+# --- Define the function to generate mini-apps based on user input ---
 def generate_mini_apps(theme):
     # Use the NLP pipeline to classify the input theme
     classification = classifier(theme)
@@ -31,18 +30,38 @@ def generate_mini_apps(theme):
             'Mood Tracker',
             'Sleep Tracker'
         ]
+    else:
+        mini_apps = ["No matching mini-apps found. Try a different theme."]
 
-# Return the generated mini-apps
+    # Return the generated mini-apps
     return mini_apps
 
-# Create the Gradio interface
+# --- Load the model and tokenizer from the provided files ---
+model = AutoModelForCausalLM.from_pretrained("./", trust_remote_code=True)  # Load from the current directory
+tokenizer = AutoTokenizer.from_pretrained("./")
+
+# --- Define a function to generate text using the model ---
+def generate_text(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    output = model.generate(**inputs, max_length=50, num_return_sequences=1)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# --- Create the Gradio interface ---
 demo = gr.Interface(
     fn=generate_mini_apps,
     inputs=gr.Textbox(label="Enter a theme for your life"),
     outputs=gr.Textbox(label="Generated Mini-Apps"),
     title="AI4ME: Personalized AI Tools",
-    description="Enter a theme for your life and we'll generate a set of AI-powered mini-apps tailored to your specific needs."
+    description="Enter your hobby/interest/job and we'll generate a set of AI-powered mini-apps tailored to your specific needs."
 )
 
-# Launch the Gradio app
-demo.launch()
+# --- Add a text generation tab ---
+with gr.Blocks() as demo_text:
+    gr.Markdown("## Text Generation")
+    input_text = gr.Textbox(label="Enter your text")
+    output_text = gr.Textbox(label="Generated Text")
+    input_text.submit(generate_text, inputs=input_text, outputs=output_text)
+
+# --- Launch the Gradio app ---
+demo.launch(share=True)  # Share the app publicly
+demo_text.launch(share=True)