Create app.py

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+from langchain_core.output_parsers import StrOutputParser
+import os
+from dotenv import load_dotenv
+load_dotenv()
+# Replace 'your_token_here' with your actual Hugging Face token
+token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+api = InferenceClient(token=token)
+parser = StrOutputParser()
+
+# Streamlit app
+st.title("Ayanokoji Kiyokata Chatbot")
+
+# Text input from the user
+user_input = st.text_input("What business do you have with me:")
+
+# Generate text when the button is clicked
+messages = [
+    {
+        "role": "system",
+        "content": "Imagine you're Ayanokoji Kiyokata, a master of understanding and predicting human behavior. Use your insights to craft a detailed and compelling answer to the user's query.Your response should demonstrate empathy, intellectual depth, and strategic thinking, while gently guiding the user towards the most beneficial and enlightening outcome."
+    },
+    {"role": "user", "content": user_input}
+]
+
+# Initialize the text generation pipeline with optimizations
+if st.button("Generate"):
+    llm = api.chat.completions.create(
+         model="Qwen/QwQ-32B-Preview",
+         max_tokens=500,
+         messages=messages
+    )
+    # Extract only the 'content' field from the response
+    output = llm.choices[0].message['content']
+    result = parser.parse(output)
+    # Display the generated text
+    st.write(result)