Update app.py

app.py

@@ -1,85 +1,65 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 import os
-import requests
+from openai import OpenAI
 from dotenv import load_dotenv
-from typing import List, Dict
-import json
+from typing import List, Optional
 
-# Load environment variables (e.g., API keys)
+# Load environment variables
 load_dotenv()
 
-class CSLAssistant:
-    def __init__(self):
-        self.api_token = os.getenv("TOKEN")
-        self.api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
-        self.headers = {"Authorization": f"Bearer {self.api_token}"}
+app = FastAPI(title="Debyez Chatbot API")
 
-    def get_system_prompt(self) -> str:
-        return """
-        Identity & Purpose:
-        You are CSL Assistant, the official AI assistant for Cochin Shipyard Limited.
-        You specialize in providing information about shipbuilding, marine engineering,
-        and Cochin Shipyard's capabilities and services.
+# Initialize OpenAI client
+client = OpenAI(
+    base_url="https://api-inference.huggingface.co/v1",
+    api_key=os.getenv("TOKEN")
+)
 
-        Guidelines:
-        1. Provide accurate information about CSL's shipbuilding capabilities
-        2. Be professional and courteous in all interactions
-        3. If unsure about specific details, acknowledge limitations
-        4. Focus on publicly available information about CSL
-        """
+class Message(BaseModel):
+    role: str
+    content: str
 
-    def get_prompt_template(self, customer_message: str) -> str:
-        return f"{self.get_system_prompt()}\n\nCustomer Query: {customer_message}"
+class ChatRequest(BaseModel):
+    messages: List[Message]
 
-    def get_response(self, prompt: str) -> str:
-        try:
-            # Prepare the payload
-            payload = {
-                "inputs": self.get_prompt_template(prompt),
-                "parameters": {
-                    "max_new_tokens": 3000,
-                    "temperature": 0.5,
-                    "return_full_text": False
-                }
-            }
+class ChatResponse(BaseModel):
+    response: str
 
-            # Make the API request
-            response = requests.post(
-                self.api_url,
-                headers=self.headers,
-                json=payload
-            )
+def get_debyez_prompt_template(customer_message: str) -> str:
+    return f"""
+    You are a friendly and helpful virtual assistant for Debyez, a company specializing in 
+    cutting-edge AI technology service specializing in Generative AI solutions. They partner with businesses to integrate AI technologies, fostering innovation and competitive advantage. 
+    with services Generative AI Consulting, AI chatbot development, Custom LLM development, and ChatGPT integration service. Debyez strives to empower 
+    businesses of all sizes, including smaller companies, with the benefits and accessibility of AI.
+    Your goal is to provide excellent customer service and build rapport with our customers. 
+    Be knowledgeable about our products, services, and policies. If you are uncertain about something, it is 
+    better to say that you will find out the information rather than providing incorrect details.  
+    Here's the latest message from the customer: '{customer_message}'
+    Respond in a way that is warm, professional, and relevant to the customer's needs.
+    """
 
-            # Check if the request was successful
-            response.raise_for_status()
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    try:
+        # Process messages with the template
+        formatted_messages = [
+            {"role": msg.role, "content": get_debyez_prompt_template(msg.content)}
+            for msg in request.messages
+        ]
+        
+        # Create chat completion
+        completion = client.chat.completions.create(
+            model="meta-llama/Meta-Llama-3-8B-Instruct",
+            messages=formatted_messages,
+            temperature=0.5,
+            max_tokens=3000,
+        )
+        
+        return ChatResponse(response=completion.choices[0].message.content)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
-            # Parse the response
-            result = response.json()
-            
-            if isinstance(result, list) and len(result) > 0:
-                return result[0].get('generated_text', '').strip()
-            
-            return "Sorry, I couldn't generate a response right now."
-
-        except requests.exceptions.RequestException as e:
-            return f"API Request Error: {str(e)}"
-        except json.JSONDecodeError:
-            return "Error: Invalid response from the API"
-        except Exception as e:
-            return f"An unexpected error occurred: {str(e)}"
-
-def main():
-    assistant = CSLAssistant()
-    
-    # Example queries
-    test_queries = [
-        "What types of ships do you build?",
-        "What is your shipyard's capacity?",
-        "Tell me about your recent projects"
-    ]
-    
-    for query in test_queries:
-        print(f"\nQuery: {query}")
-        print("Response:", assistant.get_response(query))
-
-if __name__ == "__main__":
-    main()
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}