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app.py

@@ -1,24 +1,22 @@
 
+import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-base_model = "meta‑llama/Llama‑2‑7b‑chat‑hf"   # base tokenizer
-adapter = "FinGPT/fingpt‑mt_llama2‑7b_lora"
+base = "meta-llama/Llama-2-7b-chat-hf"
+adapter = "FinGPT/fingpt-mt_llama2-7b_lora"
 
-tokenizer = AutoTokenizer.from_pretrained(base_model)
+tokenizer = AutoTokenizer.from_pretrained(base)
 model = AutoModelForCausalLM.from_pretrained(adapter, device_map="auto")
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-chat_history = []
-
 def chat(user_input, history):
     prompt = (history + "\nUser: " + user_input) if history else ("User: " + user_input)
-    output = pipe(prompt, max_new_tokens=300, do_sample=True)[0]["generated_text"]
-    history = prompt + "\nAssistant: " + output
-    return output, history
+    output = pipe(prompt, max_new_tokens=256, do_sample=True)[0]["generated_text"]
+    return output, prompt + "\nAssistant: " + output
 
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()
-    txt = gr.Textbox(placeholder="Ask a finance question...", show_label=False)
+    txt = gr.Textbox(placeholder="Ask a finance question...")
     state = gr.State("")
-    txt.submit(lambda msg, hist: (chatbot + [(msg, chat(msg, hist)[0])], chat(msg, hist)[1]), [txt, state], [chatbot, state])
+    txt.submit(lambda m, h: (chatbot + [(m, chat(m, h)[0])], chat(m, h)[1]), [txt, state], [chatbot, state])
 demo.launch()