Update app.py

app.py

@@ -47,7 +47,7 @@ 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
+    model_name = "EleutherAI/gpt-neo-2.7B"  # Model suited for longer responses
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
     return tokenizer, model
@@ -55,7 +55,7 @@ def load_model_and_tokenizer():
 tokenizer, model = load_model_and_tokenizer()
 
 # Streamlit App
-st.title("General Chatbot with Adjustable Answer Length")
+st.title("General Chatbot with Detailed Responses")
 st.write("A chatbot powered by an open-source model from Hugging Face.")
 
 # Initialize the conversation
@@ -65,26 +65,33 @@ if "conversation_history" not in st.session_state:
 # Input for user query
 user_input = st.text_input("You:", placeholder="Ask me anything...", key="user_input")
 
-# Slider for setting max response length
-max_length = st.slider("Set the maximum response length:", min_value=50, max_value=500, step=50, value=150)
+# Slider for response length
+max_length = st.slider("Set maximum response length:", min_value=100, max_value=1000, step=50, value=300)
 
 if st.button("Send") and user_input:
-    # Append user input to history
+    # Add user query to the conversation
     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")
+    # Build prompt for the model
+    prompt = f"{user_input} Please explain in detail."
+    input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors="pt")
+    
+    # Generate response
     chat_history_ids = model.generate(
-        input_ids, 
-        max_length=max_length,  # Use the user-specified max length
-        pad_token_id=tokenizer.eos_token_id
+        input_ids,
+        max_length=max_length,
+        temperature=0.9,
+        top_p=0.9,
+        repetition_penalty=1.2,
+        pad_token_id=tokenizer.eos_token_id,
+        early_stopping=False
     )
     response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
 
-    # Append model response to history
+    # Add bot response to the conversation
     st.session_state["conversation_history"].append({"role": "assistant", "content": response})
 
-# Display the conversation
+# Display conversation history
 for message in st.session_state["conversation_history"]:
     if message["role"] == "user":
         st.write(f"**You:** {message['content']}")