added data files

.gitignore

@@ -0,0 +1 @@
+venv

README.md

@@ -1,6 +1,6 @@
 ---
 title: Medicare Api
-emoji: 🐢
+emoji: 😷
 colorFrom: yellow
 colorTo: purple
 sdk: gradio

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = None 
+
+if model_id:
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,
+        device_map="auto",
+        low_cpu_mem_usage=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+else:
+    model = 'We have not downloaded model yet'
+    tokenizer = 'we have not downloaded the tokenizer yet'
+
+
+def generate_text(input_text):
+    if model_id:
+        input_ids = tokenizer.encode(input_text, return_tensors="pt")
+        attention_mask = torch.ones(input_ids.shape)
+        
+        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 = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(output_text)
+    else:
+        output_text = model
+        print(model)
+    # Remove Prompt Echo from Generated Text
+    cleaned_output_text = output_text.replace(input_text, "")
+    return cleaned_output_text
+
+
+text_generation_interface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.inputs.Textbox(label="Input Text"),
+    ],
+    outputs=gr.inputs.Textbox(label="Generated Text"),
+    title="Falcon-7B Instruct",
+).launch()

requirements.txt

@@ -0,0 +1,5 @@
+datasets
+transformers
+accelerate
+einops
+safetensors