Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load a public, open-access model
+model_name = "tiiuae/falcon-7b-instruct"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Create text-generation pipeline
+chat_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=200)
+
+# Chat function
+def chat(user_input, history=[]):
+    prompt = f"User: {user_input}\nAssistant:"
+    result = chat_pipeline(prompt)[0]["generated_text"]
+    response = result.split("Assistant:")[-1].strip()
+    history.append((user_input, response))
+    return history, history
+
+# Gradio UI
+interface = gr.ChatInterface(
+    fn=chat,
+    chatbot=gr.Chatbot(),
+    textbox=gr.Textbox(placeholder="Ask a question..."),
+    title="🗣️ Falcon 7B Chatbot",
+    description="This chatbot uses the Falcon 7B Instruct model from Hugging Face.",
+)
+
+interface.launch()