Update app.py

app.py

@@ -1,60 +1,44 @@
-import os
-import urllib.request
 import gradio as gr
-from llama_cpp import Llama
-
-
-def download_file(file_link, filename):
-    # Checks if the file already exists before downloading
-    if not os.path.isfile(filename):
-        urllib.request.urlretrieve(file_link, filename)
-        print("File downloaded successfully.")
-    else:
-        print("File already exists.")
-
-
-# Dowloading GGML model from HuggingFace
-ggml_model_path = "https://huggingface.co/CRD716/ggml-vicuna-1.1-quantized/resolve/main/ggml-vicuna-7b-1.1-q4_1.bin"
-filename = "ggml-vicuna-7b-1.1-q4_1.bin"
-
-download_file(ggml_model_path, filename)
-
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "tiiuae/falcon-7b-instruct",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+    low_cpu_mem_usage=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
 
-llm = Llama(model_path=filename, n_ctx=512, n_batch=126)
 
+def generate_text(input_text):
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    attention_mask = torch.ones(input_ids.shape)
 
-def generate_text(prompt="Who is the CEO of Apple?"):
-    output = llm(
-        prompt,
-        max_tokens=256,
-        temperature=0.1,
-        top_p=0.5,
-        echo=False,
-        stop=["#"],
+    output = model.generate(
+        input_ids,
+        attention_mask=attention_mask,
+        max_length=200,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
     )
-    output_text = output["choices"][0]["text"].strip()
+
+    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    print(output_text)
 
     # Remove Prompt Echo from Generated Text
-    cleaned_output_text = output_text.replace(prompt, "")
+    cleaned_output_text = output_text.replace(input_text, "")
     return cleaned_output_text
 
 
-description = "Vicuna-7B"
-
-examples = [
-    ["What is the capital of France?", "The capital of France is Paris."],
-    [
-        "Who wrote the novel 'Pride and Prejudice'?",
-        "The novel 'Pride and Prejudice' was written by Jane Austen.",
-    ],
-    ["What is the square root of 64?", "The square root of 64 is 8."],
-]
-
-gradio_interface = gr.Interface(
+text_generation_interface = gr.Interface(
     fn=generate_text,
-    inputs="text",
-    outputs="text",
-    examples=examples,
-    title="Vicuna-7B",
-)
-gradio_interface.launch()
+    inputs=[
+        gr.inputs.Textbox(label="Input Text"),
+    ],
+    outputs=gr.inputs.Textbox(label="Generated Text"),
+    title="Falcon-7B Instruct",
+).launch()