Update app.py

app.py

@@ -1,8 +1,11 @@
 import gradio
-from transformers import pipeline
+# from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Initialize the Hugging Face model
-model = pipeline(model='google/flan-t5-base')
+# model = pipeline(model='google/flan-t5-base')
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
 
 
 # Define the chatbot function
@@ -10,10 +13,14 @@ def chatbot(input_text):
 
     prompt = f"Give the answer of the given input in context from the bhagwat geeta. give suggestions to user which are based upon the meanings of shlok in bhagwat geeta, input = {input_text}"
     # Generate a response from the Hugging Face model
-    response = model(prompt, max_length=250, do_sample=True)[0]['generated_text'].strip()
+    # response = model(prompt, max_length=250, do_sample=True)[0]['generated_text'].strip()
+    input_text = "Write me a poem about Machine Learning."
+    input_ids = tokenizer(prompt, return_tensors="pt")
+
+    outputs = model.generate(**input_ids)
   
     # Return the bot response
-    return response
+    return outputs
 
 # Define the Gradio interface
 gradio_interface = gradio.Interface(