Create app.py

app.py

@@ -0,0 +1,75 @@
+import gradio as gr
+import torch
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Name of your public/private model on Hugging Face
+model_id = "JAZL/ChemGist-Phi3"
+
+# Fetch the secret token we just added to the Space settings
+hf_token = os.environ.get("HF_TOKEN")
+
+print(f"Loading tokenizer and model for {model_id} on CPU. This will take a few minutes...")
+
+# Pass the token to the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id, 
+    token=hf_token
+)
+
+# Pass the token to the model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cpu",
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    token=hf_token
+)
+print("Model loaded successfully into CPU memory!")
+
+def generate_answer(message: str) -> str:
+    prompt = (
+        "<|user|>\n"
+        "You are ChemGist, an intelligent and conversational chemistry assistant. Answer the user's question accurately based on your training.\n\n"
+        "Answer the user's question by weaving the following facts into a natural, flowing paragraph. "
+        "CRITICAL: Do NOT use colons. Do NOT use bullet points. Do NOT output a list. "
+        "Write in complete sentences as if explaining it to a friend.\n\n"
+        f"Question: {message}<|end|>\n"
+        "<|assistant|>\n"
+    )
+    
+    # Send inputs to CPU
+    inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
+    
+    # Generate the text
+    # NOTE: max_new_tokens is set to 200 to prevent CPU timeouts
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.3,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        do_sample=True
+    )
+    
+    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Clean up the output to only return the assistant's reply
+    if "<|assistant|>" in full_response:
+        answer = full_response.split("<|assistant|>")[-1].strip()
+    else:
+        answer = full_response.strip()
+        
+    return answer
+
+# Build the Gradio Interface
+demo = gr.Interface(
+    fn=generate_answer,
+    inputs=gr.Textbox(label="User Question", placeholder="Type your chemistry question here..."),
+    outputs=gr.Textbox(label="ChemGist Answer"),
+    title="ChemGist API Server (CPU Version)",
+    description="This Space hosts the ChemGist-Phi3 model on a Free CPU. Please be patient, CPU inference is slow and replies may take 1-3 minutes."
+)
+
+if __name__ == "__main__":
+    demo.launch()