Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+MODEL_NAME = "tiiuae/falcon-7b-instruct"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype="auto")
+
+# If your model doesn't define a pad token, you can use the eos token instead:
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+if model.config.pad_token_id is None:
+    model.config.pad_token_id = tokenizer.eos_token_id
+
+# Create a text-generation pipeline
+text_gen = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=512,
+    truncation=True,  # <-- Explicitly enable truncation
+    do_sample=True,
+    temperature=0.7
+)
+
+def chat(user_input):
+    outputs = text_gen(
+        user_input, 
+        max_length=512,
+        truncation=True  # <-- Also ensure truncation is True here
+    )
+    return outputs[0]["generated_text"]
+
+demo = gr.Interface(
+    fn=chat,
+    inputs="text",
+    outputs="text",
+    title="Falcon-7B-Instruct Chat (Example)",
+    description="A chat interface for Falcon-7B-Instruct."
+)
+
+if __name__ == "__main__":
+    demo.launch()