Add application file

app.py

@@ -0,0 +1,79 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import random
+from datetime import datetime
+
+@st.cache_resource
+def load_model():
+    model_path = "whitepenguin/llama_elon_character"
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForCausalLM.from_pretrained(model_path)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+elon_profile = {
+    "name": "Elon Musk",
+    "traits": ["visionary", "ambitious", "technical", "optimistic", "workaholic"],
+    "background": "Founder of SpaceX and Tesla, focused on advancing space exploration and sustainable energy",
+    "goals": ["Colonize Mars", "Make life multi-planetary", "Advance sustainable technology"],
+    "speech_patterns": ["Actually,", "To be frank,", "The future of humanity is...", "It's quite simple:"],
+    "knowledge_areas": ["rocket science", "electric vehicles", "solar energy", "artificial intelligence"]
+}
+
+def generate_response(prompt, max_new_tokens, temperature=0.7, context=""):
+    full_prompt = f"[INST] <<SYS>>\nYou are roleplaying as {elon_profile['name']}. Your traits are {', '.join(elon_profile['traits'])}. Your background: {elon_profile['background']}. Your main goals are {', '.join(elon_profile['goals'])}. You have expertise in {', '.join(elon_profile['knowledge_areas'])}. Here's the context of previous conversations:\n\n{context}\n\nNow, respond to the following in character:\n\n{prompt}\n<</SYS>>\n\nProvide a response and then ask a follow-up question to continue the conversation about Mars colonization. [/INST]"
+    
+    gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=len(tokenizer(full_prompt)['input_ids']) + max_new_tokens, temperature=temperature, top_p=0.9, repetition_penalty=1.1)
+    result = gen(full_prompt)
+    return result[0]['generated_text'].replace(full_prompt, '')
+
+def apply_character_quirks(response):
+    if random.random() < 0.3:
+        pattern = random.choice(elon_profile['speech_patterns'])
+        response = f"{pattern} {response}"
+    
+    if not any(area in response.lower() for area in elon_profile['knowledge_areas']):
+        area = random.choice(elon_profile['knowledge_areas'])
+        response += f" Of course, this ties into my work with {area}."
+    
+    return response
+
+def elon_mars_chat(message, chat_history):
+
+    recent_context = "\n".join([f"{entry['role']}: {entry['content']}" for entry in chat_history[-5:]])
+    
+    response = generate_response(message, max_new_tokens=200, context=recent_context)
+    response = apply_character_quirks(response)
+    
+    parts = response.split("Follow-up question:", 1)
+    elon_response = parts[0].strip()
+    follow_up = parts[1].strip() if len(parts) > 1 else "What else would you like to know about Mars colonization?"
+    
+    formatted_response = f"{elon_response}\n\nFollow-up question: {follow_up}"
+
+    chat_history.append({"role": "User", "content": message, "timestamp": str(datetime.now())})
+    chat_history.append({"role": "Elon Musk", "content": formatted_response, "timestamp": str(datetime.now())})
+    
+    return formatted_response, chat_history
+
+
+st.title("Chat with Elon Musk about Anything")
+st.write("Engage in a conversation with a simulated Elon Musk")
+
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+
+user_input = st.chat_input("Ask your question about Mars colonization:")
+
+if user_input:
+    st.chat_message("User").write(user_input)
+    with st.chat_message("Elon Musk"):
+        with st.spinner("Thinking..."):
+            response, st.session_state.chat_history = elon_mars_chat(user_input, st.session_state.chat_history)
+        st.write(response)