Update app.py

app.py

@@ -1,44 +1,16 @@
-import os
+from transformers import pipeline
 from fastapi import FastAPI
-from pydantic import BaseModel
-import dashscope
-from dashscope import Generation
-
-# Set up the API key for Dashscope
-YOUR_API_TOKEN = os.getenv('YOUR_API')
-dashscope.api_key = YOUR_API_TOKEN
-
 app = FastAPI()
 
-math_messages = []
-
-class MathQuery(BaseModel):
-    question: str
+# Load the text generation pipeline from Hugging Face
+generator = pipeline('text-generation', model='gpt2')
 
-def get_math_response(user_question):
-    global math_messages
-    if not math_messages:
-        math_messages.append({'role': 'system', 'content': 'You are a helpful math assistant.'})
-    math_messages = math_messages[:1]
-    
-    query = f"User question: {user_question}"
-    math_messages.append({'role': 'user', 'content': query})
-    
-    response = Generation.call(
-        model="qwen2-math-72b-instruct",
-        messages=math_messages,
-        result_format='message',
-        stream=False
-    )
-    
-    answer = response.output.choices[0].message.content if response.output else "No response available."
-    if answer:
-        math_messages.append({'role': 'assistant', 'content': answer})
-    
-    return answer
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!from fastapi import FastAPI"}
 
-@app.post("/math-solver")
-async def solve_math(query: MathQuery):
-    user_question = query.question
-    answer = get_math_response(user_question)
-    return {"question": user_question, "answer": answer}
+@app.get("/")
+def generate_text():
+    # Generate text from the model
+    generated = generator("once upaon aa time", max_length=100, num_return_sequences=1)
+    return {"generated_text": generated[0]['generated_text']}