Update app.py

app.py

@@ -1,27 +1,23 @@
-
-import os
-#os.system("pip install gradio[notebook]")
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-
-tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/dolphin-2_6-phi-2t5-base")
-model = AutoModelForSeq2SeqLM.from_pretrained("cognitivecomputations/dolphin-2_6-phi-2")
+# Load your teacher model
+model_name = "cognitivecomputations-dolphin-2_6-phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-def predict(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model.generate(**inputs)
-    decoded_prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return decoded_prediction
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=100)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
-iface = gr.Interface(fn=predict,
-     inputs=gr.inputs.Textbox(lines=2, 
-     placeholder="Enter your text..."),
-     outputs="text",
-     title="My First App",
-     description="you are a good teacher of python code generation student models.",
-     theme="dark",
-     analytics_enabled=False)
+interface = gr.Interface(fn=generate_response,
+                          inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
+                          outputs="text",
+                          title="Text Generation with Dolphin-2_6-Phi-2",
+                          description="This model generates responses based on the input prompt. Try it out!")
 
-iface.launch()
+if __name__ == "__main__":
+    interface.launch()