Update app.py

app.py

@@ -1,37 +1,41 @@
-# from transformers import AutoTokenizer, AutoModelForCausalLM
-# import transformers
-# import torch
-# import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+import gradio as gr
 
-# model = "tiiuae/falcon-7b-instruct"
+model = "tiiuae/falcon-7b-instruct"
 
-# tokenizer = AutoTokenizer.from_pretrained(model)
-# pipeline = transformers.pipeline(
-#     "text-generation",
-#     model=model,
-#     tokenizer=tokenizer,
-#     torch_dtype=torch.bfloat16,
-#     trust_remote_code=True,
-#     device_map="auto",
-# )
-# sequences = pipeline(
-#    "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
-#     max_length=200,
-#     do_sample=True,
-#     top_k=10,
-#     num_return_sequences=1,
-#     eos_token_id=tokenizer.eos_token_id,
-# )
-# def predict(image):
-#     predictions = pipeline(image)
-#     return {seq['generated_text'] for seq in sequences}
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+def generate_text(input_text):
+    sequences = pipeline(
+       "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
+        max_length=200,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    return sequences;
     
-# gr.Interface(
-#     predict,
-#     inputs=gr.inputs.Image(label="Ask Enything", type="filepath"),
-#     outputs=gr.outputs.Label(num_top_classes=2),
-#     title="Hot Dog? Or Not?",
-# ).launch()
-import gradio as gr
+    
+gr.Interface(
+    generate_text,
+    inputs="text",
+    outputs="text",
+    title="Text Generation App",
+    description="Generate text based on input.",
+).launch()
 
-gr.load("models/tiiuae/falcon-7b-instruct").launch()
+# def generate_text(input_text):
+#     input_ids = tokenizer.encode(input_text, return_tensors="pt")
+#     output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+#     generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+#     return generated_text