Update app.py

app.py

@@ -1,30 +1,43 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load the Mistral-7B model using pipeline
-pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2")
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
+
+# Initialize session state to store chat history
+if "chat_history" not in st.session_state:
+    st.session_state["chat_history"] = []
 
 def generate_response(prompt):
-  """Generates a response using the Mistral-7B model."""
-  response = pipe(prompt, max_length=1000, num_return_sequences=1)[0]["generated_text"]
-  return response.strip()
+  """Generates a response from the model based on the prompt."""
+  input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors="pt")
+  beam_output = model.generate(
+      input_ids,
+      max_length=50,
+      num_beams=5,
+      no_repeat_ngram_size=2,
+      early_stopping=True
+  )
+  return tokenizer.decode(beam_output[0], skip_special_tokens=True)
+
+def display_chat_history():
+  """Displays the chat history in the Streamlit app."""
+  for message in st.session_state["chat_history"]:
+    st.write(message)
 
 # Streamlit app layout
 st.title("Mistral Chatbot")
-user_input = st.text_input("Ask me anything!")
+display_chat_history()
+
+user_input = st.text_input("You:")
 
 if user_input:
+  # Add user input to chat history
+  st.session_state["chat_history"].append(f"You: {user_input}")
+
+  # Generate response from the model
   response = generate_response(user_input)
+  st.session_state["chat_history"].append(f"Mistral: {response}")
   st.write(f"Mistral: {response}")
 
-# Deployment to Hugging Face Spaces (instructions included)
-# 1. Create a Hugging Face account (if you don't have one)
-# 2. Create a new Space from your account
-# 3. Push your code to a Git repository (e.g., GitHub)
-# 4. In your Space settings, connect your Git repository
-# 5. Under "Model", select the Mistral-7B model you're using
-# 6. Under "Environment", create a new environment with Python 3.7+
-# 7. Under "Requirements", add "streamlit transformers" (separate lines)
-# 8. Under "Start script", enter "streamlit run app.py" (replace app.py with your filename)
-# 9. Deploy your Space!
-