Update app.py

app.py

@@ -1,46 +1,3 @@
-# import streamlit as st
-# from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# # Load the model and tokenizer
-# @st.cache_resource
-# def load_model_and_tokenizer():
-#     model_name = "microsoft/DialoGPT-medium"  # Replace with your chosen model
-#     tokenizer = AutoTokenizer.from_pretrained(model_name)
-#     model = AutoModelForCausalLM.from_pretrained(model_name)
-#     return tokenizer, model
-
-# tokenizer, model = load_model_and_tokenizer()
-
-# # Streamlit App
-# st.title("General Chatbot")
-# st.write("A chatbot powered by an open-source model from Hugging Face.")
-
-# # Initialize the conversation
-# if "conversation_history" not in st.session_state:
-#     st.session_state["conversation_history"] = []
-
-# # Input box for user query
-# user_input = st.text_input("You:", placeholder="Ask me anything...", key="user_input")
-
-# if st.button("Send") and user_input:
-#     # Append user input to history
-#     st.session_state["conversation_history"].append({"role": "user", "content": user_input})
-    
-#     # Tokenize and generate response
-#     input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
-#     chat_history_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-#     response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
-
-#     # Append model response to history
-#     st.session_state["conversation_history"].append({"role": "assistant", "content": response})
-
-# # Display the conversation
-# for message in st.session_state["conversation_history"]:
-#     if message["role"] == "user":
-#         st.write(f"**You:** {message['content']}")
-#     else:
-#         st.write(f"**Bot:** {message['content']}")
-
 import streamlit as st
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -58,7 +15,7 @@ tokenizer, model = load_model_and_tokenizer()
 st.title("General Chatbot")
 st.write("A chatbot powered by an open-source model from Hugging Face.")
 
-# Initialize the conversation history
+# Initialize the conversation
 if "conversation_history" not in st.session_state:
     st.session_state["conversation_history"] = []
 
@@ -68,30 +25,13 @@ user_input = st.text_input("You:", placeholder="Ask me anything...", key="user_i
 if st.button("Send") and user_input:
     # Append user input to history
     st.session_state["conversation_history"].append({"role": "user", "content": user_input})
-
-    # Prepare the input for the model
-    conversation_context = ""
-    for message in st.session_state["conversation_history"]:
-        if message["role"] == "user":
-            conversation_context += f"User: {message['content']}\n"
-        elif message["role"] == "assistant":
-            conversation_context += f"Bot: {message['content']}\n"
-
-    input_ids = tokenizer.encode(conversation_context + "Bot:", return_tensors="pt")
-
-    # Generate the response with adjusted parameters
-    chat_history_ids = model.generate(
-        input_ids,
-        max_length=500,  # Increase maximum length for longer responses
-        num_return_sequences=1,
-        temperature=0.2,  # Adjust for creativity (lower is more focused, higher is more diverse)
-        top_p=0.5,  # Use nucleus sampling for diversity
-        top_k=50,  # Limit to top-k tokens for more controlled output
-        pad_token_id=tokenizer.eos_token_id
-    )
-
-    # Decode the response and add it to history
+    
+    # Tokenize and generate response
+    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
+    chat_history_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
     response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+    # Append model response to history
     st.session_state["conversation_history"].append({"role": "assistant", "content": response})
 
 # Display the conversation
@@ -100,3 +40,4 @@ for message in st.session_state["conversation_history"]:
         st.write(f"**You:** {message['content']}")
     else:
         st.write(f"**Bot:** {message['content']}")
+