first commit

first try on both gradio and huggingface spaces.

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import snapshot_download
+
+# Set device to CPU
+device = "cpu"
+repo_id = 'amgadhasan/phi-2'
+model_path = snapshot_download(repo_id=repo_id, repo_type="model", local_dir="./phi-2", use_auth_token=False)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+# Set default dtype to float32 for compatibility with CPU
+torch.set_default_dtype(torch.float32)
+
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True)
+
+def generate(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    outputs = model.generate(**inputs, max_length=200)
+    completion = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return completion
+
+def ask_question(user_question):
+    if user_question.lower() == 'quit':
+        return "Session ended. Goodbye!"
+    else:
+        # Here, we're explicitly setting the context for an academic answer.
+        prompt = f"Academic response to the question about basic science subjects: {user_question}"
+        answer = generate(prompt)
+        return answer
+
+iface = gr.Interface(fn=ask_question, inputs="text", outputs="text")
+iface.launch(share=True)