Create app.py

app.py

@@ -0,0 +1,45 @@
+import os
+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import uvicorn
+
+# Define a Pydantic model for request validation
+class Query(BaseModel):
+    text: str
+
+# Initialize FastAPI app
+app = FastAPI(title="Financial Chatbot API")
+
+# Load your fine-tuned model and tokenizer
+model_name = "Phoenix21/llama-3-2-3b-finetuned-finance"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Create a text-generation pipeline
+chat_pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=256,
+    temperature=0.7,
+    top_p=0.95,
+)
+
+# Define an endpoint for generating responses
+@app.post("/generate")
+def generate(query: Query):
+    prompt = f"Question: {query.text}\nAnswer: "
+    response = chat_pipe(prompt)[0]["generated_text"]
+    return {"response": response}
+
+# Run the app using uvicorn. Hugging Face Spaces sets the PORT environment variable.
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 8000))
+    uvicorn.run(app, host="0.0.0.0", port=port)