Update app.py

app.py

@@ -1,16 +1,45 @@
-from transformers import pipeline
+import os
 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
+print(dashscope.api_key)
 app = FastAPI()
 
-# Load the text generation pipeline from Hugging Face
-generator = pipeline('text-generation', model='gpt2')
+math_messages = []
+
+class MathQuery(BaseModel):
+    question: str
+
+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']}