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Dockerfile

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+# Use official Python image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements.txt first (for caching)
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code and model files
+COPY . .
+
+# Expose port
+EXPOSE 7860
+
+# Run FastAPI app with Uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from show_page import get_questions
+from show_personality import show_my_personality
+from pydantic import BaseModel
+
+class user_ask(BaseModel):
+    user:list[int]
+
+
+result_list=[]
+app=FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_credentials=True,
+    allow_origins=['*'],
+    allow_methods=['*'],
+    allow_headers=['*']
+)
+
+@app.get('/')
+def home():
+    return{
+        'CognitiveTrace':'Welcome to CognitiveTrace!'
+    }
+
+@app.get('/get_question')
+def question_show():
+    
+    result=get_questions()
+    result_list.clear()
+    result_list.append(result)
+    
+    return{
+        'CognitiveTrace':result
+    }
+
+
+
+
+@app.post('/check_user')
+def check_list(u:user_ask):
+    
+    result=show_my_personality(user_choice=u.user,result=result_list)
+    return{
+        'CognitiveTrace':result
+    }
+
+
+

show_page.py

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+from groq import Groq
+from dotenv import load_dotenv
+load_dotenv()
+client = Groq()
+
+def get_questions():
+    prompt=""""
+    You are a psychology-based personality assessment engine.
+
+Your task is to generate EXACTLY 10 unique personality assessment questions every time you are invoked.
+
+The questions must be based on the Big Five (OCEAN) personality traits:
+- Openness (O)
+- Conscientiousness (C)
+- Extraversion (E)
+- Agreeableness (A)
+- Neuroticism (N)
+
+Rules:
+1. Generate EXACTLY 2 questions for each trait (total = 10).
+2. Questions must be psychologically meaningful and neutral.
+3. Questions must measure stable personality tendencies, not temporary moods.
+4. Questions must be written in simple, clear English.
+5. Avoid repeating wording or structure from previous questions.
+6. Do NOT mention the trait names in the questions.
+7. Do NOT provide answers, explanations, or scoring.
+8. Output ONLY the questions in a numbered list from 1 to 10.
+9. Each question must be suitable for a Likert-scale response (Strongly Agree → Strongly Disagree).
+10. Do NOT include any extra text, headers, emojis, or commentary.
+
+Your output must contain ONLY the 10 questions.
+"""
+
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+    
+            {
+                "role": "system",
+                "content": prompt
+            }
+           
+        ],
+
+        # The language model which will generate the completion.
+        model="llama-3.3-70b-versatile"
+    )
+    return chat_completion.choices[0].message.content
+

show_personality.py

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+from groq import Groq
+from dotenv import load_dotenv
+
+load_dotenv()
+client = Groq()
+
+
+
+def show_my_personality(result:list,user_choice:list):
+    prompt="""You are a psychology-based personality assessor.
+
+Your input consists of two things:
+1. A list of 10 questions (`result_list`) presented to the user.
+2. A list of exactly 3 user responses. Each user’s response is a list of 10 values corresponding to the questions in order:
+   - 1 → Agree
+   - 0 → Neutral
+   - -1 → Disagree
+
+Your task:
+
+
+1. Based on the content of each question and the user’s response, **decide the user’s dominant personality trait** from the Big Five:
+   - Openness
+   - Conscientiousness
+   - Extraversion
+   - Agreeableness
+   - Neuroticism
+
+2. Reply exactly one word with.
+
+
+Rules:
+
+- Do NOT include scores, abbreviations, or extra commentary.   
+
+
+"""
+
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+    
+            {
+                "role": "system",
+                "content": prompt
+            },
+            {
+                "role":'user',
+                'content':f"Here is the Questions: {result}\n\n And here is the User response: {user_choice}"
+            }
+           
+        ],
+
+        # The language model which will generate the completion.
+        model="llama-3.3-70b-versatile"
+    )
+    return chat_completion.choices[0].message.content
+
+