Update app.py

app.py

@@ -1,42 +1,42 @@
-# import google.generativeai as palm
+import google.generativeai as palm
 import streamlit as st 
-# import os 
+import os 
 
-# # Set your API key
-# palm.configure(api_key = os.environ['PALM_KEY'])
+# Set your API key
+palm.configure(api_key = os.environ['PALM_KEY'])
 
-# # Select the PaLM 2 model
-# model = 'models/text-bison-001'
+# Select the PaLM 2 model
+model = 'models/text-bison-001'
 
-# # Generate text
-# if prompt := st.chat_input("Ask your query..."):
-#     enprom = f"""Act as bhagwan krishna and Answer the below provided input in context to Bhagwad Geeta. Use the verses and chapters sentences as references to your answer with suggestions
-#     coming from Bhagwad Geeta. Your answer to below input should only be in context to Bhagwad geeta.\nInput= {prompt}"""
-#     completion = palm.generate_text(model=model, prompt=enprom, temperature=0.5, max_output_tokens=800)
+# Generate text
+if prompt := st.chat_input("Ask your query..."):
+    enprom = f"""Act as bhagwan krishna and Answer the below provided input in context to Bhagwad Geeta. Use the verses and chapters sentences as references to your answer with suggestions
+    coming from Bhagwad Geeta. Your answer to below input should only be in context to Bhagwad geeta.\nInput= {prompt}"""
+    completion = palm.generate_text(model=model, prompt=enprom, temperature=0.5, max_output_tokens=800)
 
-# # response = palm.chat(messages=["Hello."])
-# # print(response.last) #  'Hello! What can I help you with?'
-# # response.reply("Can you tell me a joke?")
+# response = palm.chat(messages=["Hello."])
+# print(response.last) #  'Hello! What can I help you with?'
+# response.reply("Can you tell me a joke?")
 
-# # Print the generated text
-#     with st.chat_message("Assistant"):
-#         st.write(prompt)
-#         st.write(completion.result)
+# Print the generated text
+    with st.chat_message("Assistant"):
+        st.write(prompt)
+        st.write(completion.result)
 
 
 
 
 
-from transformers import AutoTokenizer, AutoModelForCausalLM
+# from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
-model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
+# tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+# model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
 
-input_text = "Write me a poem about Machine Learning."
-input_ids = tokenizer(input_text, return_tensors="pt")
+# input_text = "Write me a poem about Machine Learning."
+# input_ids = tokenizer(input_text, return_tensors="pt")
 
-outputs = model.generate(**input_ids)
-st.write(tokenizer.decode(outputs[0]))
+# outputs = model.generate(**input_ids)
+# st.write(tokenizer.decode(outputs[0]))