app

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "kursathalat/gemma-pragma"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Streamlit app
+st.title("Hugging Face Chatbot")
+
+# Initialize the session state for history if it doesn't exist
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# User input
+user_input = st.text_input("You:", "")
+
+# Respond to user input
+if user_input:
+    # Tokenize input
+    inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
+
+    # Get the bot's response
+    if len(st.session_state.history) > 0:
+        past_inputs = tokenizer.encode(" ".join(st.session_state.history) + tokenizer.eos_token, return_tensors="pt")
+        inputs = torch.cat([past_inputs, inputs], dim=-1)
+        
+    outputs = model.generate(inputs, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(outputs[:, inputs.shape[-1]:][0], skip_special_tokens=True)
+
+    # Update chat history
+    st.session_state.history.append(user_input)
+    st.session_state.history.append(response)
+
+    # Display chat history
+    for i, msg in enumerate(st.session_state.history):
+        if i % 2 == 0:
+            st.write(f"You: {msg}")
+        else:
+            st.write(f"Bot: {msg}")

requirements.txt

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+transformers
+torch