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Dockerfile

@@ -8,4 +8,4 @@ RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
 COPY . .
 
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -0,0 +1,113 @@
+# main.py
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Optional
+import openai
+
+app = FastAPI()
+
+# Define models for data validation
+class Summarize(BaseModel):
+    diagnose: str
+    cause: str
+    solution: str
+
+class StoredResponse(BaseModel):
+    selected_response: str
+    diagnose: str
+    cause: str
+    solution: str
+
+# Initialize OpenAI API key
+openai.api_key = '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'
+
+# Database to store stored responses (simulated in-memory database)
+stored_responses = []
+
+# Function to check if a stored response exists for a given input
+def find_stored_response(diagnose: str, cause: str, solution: str) -> Optional[StoredResponse]:
+    for response in stored_responses:
+        if (
+            response.diagnose == diagnose
+            and response.cause == cause
+            and response.solution == solution
+        ):
+            return response
+    return None
+
+# Summarize endpoint
+@app.post("/summarize/", response_model=dict)
+async def summarize(item: Summarize):
+    # Check if a stored response exists for the input
+    existing_response = find_stored_response(item.diagnose, item.cause, item.solution)
+    if existing_response:
+        return [existing_response.selected_response]
+
+    # Use OpenAI's GPT-3 to generate a summary
+    user_input = (
+        f"Diagnose: {item.diagnose}\n"
+        f"Cause: {item.cause}\n"
+        f"Solution: {item.solution}\n"
+    )
+    prompt = "Translate and summarize Norwegian inputs about 'Diagnose', 'Cause', and 'Løsning og resultat' into English outputs of 'Diagnosis' and 'Solution', each strictly limited to 72 characters. When using domain-specific terminology, refer to the dictionary to understand or include relevant acronyms: acronym_dictionary = { OBC: On-Board Charger, E_VCU: Electronic Vehicle Control Unit, TSB: Technical Service Bulletin, (Examples) U2FFF / B1004 96 / U1F2F 87 / P1B74 / P000A 00: This is an error code that was registered in the fault log of the vehicle., TSB D1BW011FQ0: Code, HV battery: High Volt battery, ECU: Engine Control Unit, VOR: Vehicle Off Road, DID: Deal Issue Detection, DID-I: Dealer Issue Detection Incident, DID-A: Dealer Issue Detection Assistance, DID-S: Dealer Issue Detection Safety, EV-ECU: Electric Vehicle ECU, BMU or TBMU: Traction Battery ECU, HPCU: Hybrid Control Unit, MCU: Inverter, iSolve: Repair Solution, DPF: Diesel Particle Filter, EGR: Exhaust Gas Recirculation, DA: Documents (Sharing Platform), Reman: Remanufactured Parts, DTC: Diagnostic Trouble Code, DARS: Documents }.Always mention TSB, mentioned codes and Kilometers or units with details.GIVE 3 DIFFERENT RESPONSES IN NEW LINES WITH SPECIAL NUMBERINGS LIKE #1 #2 #3. Example: #1. Diagnosis: Text. \nSolution: Text.\n#2. Diagnosis: Text. \nSolution: Text.\n#3. Diagnosis: Text. \nSolution: Text."
+    
+    response = openai.Completion.create(
+    engine="text-davinci-002",
+    prompt=prompt + "\n" + user_input,
+    max_tokens=3000
+)
+    
+    gpt_response = response.choices[0].text.strip()
+    response_parts = gpt_response.split('\n#')
+    
+    if len(response_parts) >= 3:
+        response1 = response_parts[0]
+        response2 = response_parts[1]
+        response3 = response_parts[2]
+    else:
+        response1 = response_parts[0] if len(response_parts) >= 1 else ""
+        response2 = response_parts[1] if len(response_parts) >= 2 else ""
+        response3 = ""
+
+    if response1.startswith("#"):
+        response1 = response1[1:]
+    else:
+        response1 = response1
+
+    response1 = response1.strip()
+    response2 = response2.strip()
+    response3 = response3.strip()
+
+    response_dict = {
+        "Response 1": response1,
+        "Response 2": response2,
+        "Response 3": response3
+    }
+
+    return response_dict
+
+# Store Response endpoint
+@app.post("/store-response/", response_model=StoredResponse)
+async def store_selected_response(item: StoredResponse):
+    # Check if a stored response with the same input already exists
+    existing_response = find_stored_response(item.diagnose, item.cause, item.solution)
+    if existing_response:
+        # Update the existing response
+        existing_response.selected_response = item.selected_response
+        return existing_response
+
+    # If no matching stored response, create a new one
+    stored_responses.append(item)
+    return item
+
+# Check Response endpoint
+@app.post("/check-response/", response_model=StoredResponse)
+async def check_stored_response(item: Summarize):
+    # Check if a stored response exists for the input
+    stored_response = find_stored_response(item.diagnose, item.cause, item.solution)
+    if stored_response:
+        return stored_response
+    
+    # If no stored response found, raise an HTTPException with 404 status code
+    raise HTTPException(status_code=404, detail="No stored response found")

requirements.txt

@@ -0,0 +1,3 @@
+greenlet==3.0.1
+SQLAlchemy==2.0.23
+typing_extensions==4.8.0