Create app.py

app.py

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+import os
+# Limit parallelism to fit 2 CPU cores
+os.environ["OMP_NUM_THREADS"] = "2"
+os.environ["MKL_NUM_THREADS"] = "2"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from fastapi import FastAPI, HTTPException
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import gradio as gr
+
+# Load the Phi-1.5 Instruct model (1.3B) from Hugging Face
+model_id = "rasyosef/Phi-1_5-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+pipe = pipeline(
+    "text-generation", 
+    model=model, 
+    tokenizer=tokenizer
+)
+
+app = FastAPI()
+
+@app.get("/chat")
+def chat(query: str):
+    """
+    REST API endpoint. Use: GET /chat?query=Your question
+    Returns a JSON {"response": "..."}.
+    """
+    if not query:
+        raise HTTPException(status_code=400, detail="Query parameter 'query' is required.")
+    # Use the same prompt format expected by the model:
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user",   "content": query}
+    ]
+    result = pipe(
+        messages, 
+        max_new_tokens=100, 
+        do_sample=False, 
+        return_full_text=False
+    )
+    answer = result[0]["generated_text"].strip()
+    return {"response": answer}
+
+# Define Gradio UI (optional)
+def gradio_chat(input_text):
+    if not input_text:
+        return ""
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user",   "content": input_text}
+    ]
+    result = pipe(messages, max_new_tokens=100, do_sample=False, return_full_text=False)
+    return result[0]["generated_text"].strip()
+
+iface = gr.Interface(
+    fn=gradio_chat, 
+    inputs=gr.Textbox(lines=2, placeholder="Type a message..."), 
+    outputs="text",
+    title="Phi-1.5 Chatbot",
+    description="Enter a message and press **Submit** to get a response."
+)
+
+# Mount Gradio at root so it does not conflict with /chat
+app = gr.mount_gradio_app(app, iface, path="/")