Update app.py

app.py

@@ -1,56 +1,39 @@
-import subprocess
-import sys
-import gradio as gr
-from model import llm_chain_response, get_response_value
-from process_documents import create_db_from_files
-
-def install(package):
-    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
-
-# List of packages to install
-packages = [
-    "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git",
-    "--no-deps xformers",
-    "trl<0.9.0",
-    "peft",
-    "accelerate",
-    "bitsandbytes"
-]
-
-# Install packages
-for package in packages:
-    try:
-        install(package)
-    except Exception as e:
-        print(f"Failed to install {package}: {e}")
- 
-llm_chain = llm_chain_response()
-
-def chat_with_mistral(user_input):
-    if not user_input:
-      return "The message is not be empty."
-    response = llm_chain.invoke({"query": user_input})
-    print(response)
-
-    print("---------------Response--------------")
-    print(get_response_value(response["result"]))
-    return get_response_value(response["result"])
-
-def main():
-    # Initialize the database
-    create_db_from_files()
-
-    # Set up and launch the Gradio interface
-    iface = gr.Interface(
-        fn=chat_with_mistral,
-        inputs=gr.components.Textbox(label="Enter Your Message"),
-        outputs=gr.components.Markdown(label="ChatbotResponse"),
-        title="Resvu AI Chatbot",
-        description="Interact with the Resvu API via this chatbot. Enter a message and get a response.",
-        examples=["Hi, how are you", "Who are you?", "What services do you offer?", "How can I find out about upcoming community events?"],
-        allow_flagging="never"
-    )
-    iface.launch()
-
-if __name__ == "__main__":
+import subprocess
+import sys
+import gradio as gr
+from model import llm_chain_response, get_response_value
+from process_documents import create_db_from_files
+
+!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+!pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes
+ 
+llm_chain = llm_chain_response()
+
+def chat_with_mistral(user_input):
+    if not user_input:
+      return "The message is not be empty."
+    response = llm_chain.invoke({"query": user_input})
+    print(response)
+
+    print("---------------Response--------------")
+    print(get_response_value(response["result"]))
+    return get_response_value(response["result"])
+
+def main():
+    # Initialize the database
+    create_db_from_files()
+
+    # Set up and launch the Gradio interface
+    iface = gr.Interface(
+        fn=chat_with_mistral,
+        inputs=gr.components.Textbox(label="Enter Your Message"),
+        outputs=gr.components.Markdown(label="ChatbotResponse"),
+        title="Resvu AI Chatbot",
+        description="Interact with the Resvu API via this chatbot. Enter a message and get a response.",
+        examples=["Hi, how are you", "Who are you?", "What services do you offer?", "How can I find out about upcoming community events?"],
+        allow_flagging="never"
+    )
+    iface.launch()
+
+if __name__ == "__main__":
     main()