yeah

app.py

@@ -1,23 +1,38 @@
 import gradio as gr
-from transformers import pipeline
+import requests
 
-# Load your Hugging Face model
-# pipe = pipeline("text-classification", model="davidgaofc/TechDebtClassifier")
-  # Replace 'gpt2' with your model
-def pipe(temp, temp2):
-    return temp
+# Set your Hugging Face API key here
+API_KEY = "your_huggingface_api_key"
 
-def predict(input_text):
-    # Generate output using the model
-    output = pipe(input_text, max_length=2000)  # Adjust parameters as needed
-    return output[0]['generated_text']
+def generate_text(input_text):
+    return input_text
 
-# Create the Gradio interface
-interface = gr.Interface(fn=predict,
-                         inputs=gr.Textbox(lines=2, placeholder="Type something here..."),
-                         outputs='text',
-                         title="Hugging Face Model Inference",
-                         description="Type in some text and see how the model responds!")
+def analyze_sentiment(input_text):
+    return input_text
+
+def echo_text(input_text):
+    return input_text
+
+# Define the Gradio interface
+interface = gr.Interface(
+    [
+        generate_text,  # Text generation
+        analyze_sentiment,  # Sentiment analysis
+        echo_text  # Echo text
+    ],
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter text for generation..."),
+        gr.Textbox(lines=2, placeholder="Enter text for sentiment analysis..."),
+        gr.Textbox(lines=2, placeholder="Enter text to echo...")
+    ],
+    outputs=[
+        "text",
+        "text",
+        "text"
+    ],
+    title="Multiple Hugging Face Models",
+    description="Three different functions: Text generation, Sentiment Analysis, and Echo."
+)
 
 if __name__ == "__main__":
     interface.launch()