Create app.py

app.py

@@ -0,0 +1,370 @@
+import os
+import streamlit as st
+from openai import OpenAI
+import base64
+import json
+import requests
+import re
+import pandas as pd
+from huggingface_hub import InferenceClient
+
+HF_MODEL_MISTRAL = "mistralai/Mistral-7B-Instruct-v0.3"
+HF_MODEL_LLAMA = "meta-llama/Llama-3.3-70B-Instruct"
+
+PROMPTS_DOC_URL = "https://docs.google.com/document/d/17rB_0BGQ4DGT7pwOV8O58sNvzBWgywq6ZDgzyJ9pmjs/export?format=txt"
+
+STEP1_SYSTEM_PROMPT = "STEP1 SYSPROMPT"
+STEP1_USER_PROMPT = "STEP1 USERPROMPT"
+STEP2_SYSTEM_PROMPT = "STEP2 SYSPROMPT"
+STEP2_USER_PROMPT = "STEP2 USERPROMPT"
+STEP3A_SYSTEM_PROMPT = "STEP3A SYSPROMPT"
+STEP3A_USER_PROMPT = "STEP3A USERPROMPT"
+STEP3B_SYSTEM_PROMPT = "STEP3B SYSPROMPT"
+STEP3B_USER_PROMPT = "STEP3B USERPROMPT"
+
+
+def fetch_prompts_from_google_doc():
+    response = requests.get(PROMPTS_DOC_URL)
+    if response.status_code != 200:
+        raise Exception("Failed to fetch document")
+    
+    text = response.text
+    prompts = {}
+
+    pattern = r"\{BEGIN (.*?)\}([\s\S]*?)\{END \1\}"
+    matches = re.findall(pattern, text)
+
+    for key, content in matches:
+        prompts[key.strip()] = content.strip()
+
+    return prompts
+
+# Step 1: Extract PlantUML Code
+def extract_plantuml_code(client_openai, uploaded_file, model_choice, prompts):
+
+    st.write("Model: ", model_choice)
+
+    encoded_image = base64.b64encode(uploaded_file.getvalue()).decode("utf-8")
+
+    response = client_openai.chat.completions.create(
+        model=model_choice,
+        messages=[
+            {
+                "role": "system",
+                "content": prompts[STEP1_SYSTEM_PROMPT],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompts[STEP1_USER_PROMPT]},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{encoded_image}"},
+                    },
+                ],
+            },
+        ],
+        temperature=0.2,
+        top_p=0.1,
+        max_tokens=4096,
+    )
+
+    return response.choices[0].message.content
+
+
+# Step 2: Compare PlantUML Code
+def compare_plantuml(client_openai, client_hf_mistral, client_hf_llama, plantuml_instructor, plantuml_student, model_choice, prompts):
+
+    st.write("Model: ", model_choice)
+
+    user_prompt=f"""
+    {prompts[STEP2_USER_PROMPT]}
+    
+    **Instructor's UML:**
+    {plantuml_instructor}
+
+    **Student's UML:**
+    {plantuml_student}
+    """
+
+    if model_choice in [HF_MODEL_MISTRAL]:
+        response = client_hf_mistral.chat_completion(
+            [
+                {
+                    "role": "system",
+                    "content": prompts[STEP2_SYSTEM_PROMPT],
+                },
+                {"role": "user", "content": user_prompt},
+            ],
+            max_tokens=1024,
+            temperature=0.2,
+        )
+        return response["choices"][0]["message"]["content"]
+    
+    elif model_choice in [HF_MODEL_LLAMA]:
+        response = client_hf_llama.chat_completion(
+            [
+                {
+                    "role": "system",
+                    "content": prompts[STEP2_SYSTEM_PROMPT],
+                },
+                {"role": "user", "content": user_prompt},
+            ],
+            max_tokens=1024,
+            temperature=0.2,
+        )
+        return response["choices"][0]["message"]["content"]
+    
+    else:
+        response = client_openai.chat.completions.create(
+            model=model_choice,
+            messages=[
+                {
+                    "role": "system",
+                    "content": prompts[STEP2_SYSTEM_PROMPT],
+                },
+                {
+                    "role": "user",
+                    "content": user_prompt,
+                },
+            ],
+            temperature=0.2,
+            top_p=0.1,
+            max_tokens=4096,
+        )
+        return response.choices[0].message.content
+
+# Step 3A: Generate Student Feedback
+def generate_student_feedback(client_openai, client_hf_mistral, client_hf_llama, differences, model_choice, prompts):
+
+    st.write("Model (Student Feedback):", model_choice)  
+
+    user_prompt=f"""
+    {prompts[STEP3A_USER_PROMPT]}
+    {json.dumps(differences, indent=2)}
+    """
+
+    if model_choice in [HF_MODEL_MISTRAL]:
+        response = client_hf_mistral.chat_completion(
+            [
+                {
+                    "role": "system",
+                    "content": prompts[STEP3A_SYSTEM_PROMPT],
+                },
+                {"role": "user", "content": user_prompt},
+            ],
+            max_tokens=1024,
+            temperature=0.2,
+        )
+
+        return response["choices"][0]["message"]["content"]
+      
+    elif model_choice in [HF_MODEL_LLAMA]:
+        response = client_hf_llama.chat_completion(
+            [
+                {
+                    "role": "system",
+                    "content": prompts[STEP3A_SYSTEM_PROMPT],
+                },
+                {"role": "user", "content": user_prompt},
+            ],
+            max_tokens=1024,
+            temperature=0.2,
+        )
+
+        return response["choices"][0]["message"]["content"]
+    
+    else:
+        response = client_openai.chat.completions.create(
+            model=model_choice,
+            messages=[
+                {
+                    "role": "system",
+                    "content": prompts[STEP3A_SYSTEM_PROMPT],
+                },
+                {
+                    "role": "user",
+                    "content": user_prompt,
+                },
+            ],
+            temperature=0.2,
+            top_p=0.1,
+            max_tokens=4096,
+        )
+
+        return response.choices[0].message.content
+
+
+# Step 3B: Generate Educator Feedback
+def generate_educator_feedback(client_openai, client_hf_mistral, client_hf_llama, differences, model_choice, prompts):
+  
+    st.write("Model (Educator Feedback): ", model_choice)
+
+    user_prompt=f"""
+    {prompts[STEP3B_USER_PROMPT]}
+    {json.dumps(differences, indent=2)}
+    """
+
+    if model_choice in [HF_MODEL_MISTRAL]:
+        response = client_hf_mistral.chat_completion(
+            [
+                {
+                    "role": "system",
+                    "content": prompts[STEP3B_SYSTEM_PROMPT],
+                },
+                {"role": "user", "content": user_prompt},
+            ],
+            max_tokens=1024,
+            temperature=0.2,
+        )
+
+        return response["choices"][0]["message"]["content"]
+      
+    elif model_choice in [HF_MODEL_LLAMA]:
+        response = client_hf_llama.chat_completion(
+            [
+                {
+                    "role": "system",
+                    "content": prompts[STEP3B_SYSTEM_PROMPT],
+                },
+                {"role": "user", "content": user_prompt},
+            ],
+            max_tokens=1024,
+            temperature=0.2,
+        )
+
+        return response["choices"][0]["message"]["content"]
+    
+    else:
+        response = client_openai.chat.completions.create(
+            model=model_choice,
+            messages=[
+                {
+                    "role": "system",
+                    "content": prompts[STEP3B_SYSTEM_PROMPT],
+                },
+                {
+                    "role": "user",
+                    "content": user_prompt,
+                },
+            ],
+            temperature=0.2,
+            top_p=0.1,
+            max_tokens=4096,
+        )
+        
+        st.write(response)
+
+        return response.choices[0].message.content
+
+
+# Streamlit app layout
+st.title("LLM-based Analysis and Feedback of a UML Diagram")
+st.write("The pipeline consists of three steps:")
+st.write("1. Extract PlantUML code from the uploaded UML diagrams using GPT-4o or GPT-4o Mini.")
+st.write("2. Compare the extracted PlantUML code.")
+st.write("3. Analyse the differences and present them in a structured format.")
+
+prompts = fetch_prompts_from_google_doc()
+
+openai_api_key = st.text_input("OpenAI API key", type="password")
+hf_api_key = st.text_input("Hugging Face API key", type="password")
+
+if openai_api_key and hf_api_key:
+    client_openai = OpenAI(api_key=openai_api_key)
+    client_hf_mistral = InferenceClient(model=HF_MODEL_MISTRAL, token=hf_api_key)
+    client_hf_llama = InferenceClient(model=HF_MODEL_LLAMA, token=hf_api_key)
+    
+    model_choice_step1 = st.selectbox("Select the model for Step 1", ["gpt-4o", "gpt-4o-mini"])
+    model_choice_step2 = st.selectbox("Select the model for Step 2", [HF_MODEL_MISTRAL, HF_MODEL_LLAMA, "gpt-4o", "gpt-4o-mini"])
+    model_choice_step3 = st.selectbox("Select the model for Step 3", [HF_MODEL_MISTRAL, HF_MODEL_LLAMA, "gpt-4o", "gpt-4o-mini"])
+
+    st.subheader("Step 1: PlantUML Code Extraction using GPT-4o or GPT-4o Mini")
+
+    col1, col2 = st.columns(2)
+    with col1:
+        uploaded_instructor_solution = st.file_uploader(
+            "Upload Instructor UML Diagram", type=["jpg", "jpeg", "png"]
+        )
+    with col2:
+        uploaded_student_solution = st.file_uploader(
+            "Upload Student UML Diagram", type=["jpg", "jpeg", "png"]
+        )
+
+    if (uploaded_instructor_solution is not None and uploaded_student_solution is not None):
+        try:
+            with st.spinner(
+                "Extracting PlantUML code from the uploaded UML diagrams..."
+            ):
+                with col1:
+                    st.image(
+                        uploaded_instructor_solution,
+                        caption="Uploaded Instructor UML Diagram",
+                        use_container_width=True,
+                    )
+                    st.write("")
+                    plantuml_instructor_solution = extract_plantuml_code(
+                        client_openai, uploaded_instructor_solution, model_choice_step1, prompts
+                    )
+                with col2:
+                    st.write("")
+                    st.image(
+                        uploaded_student_solution,
+                        caption="Uploaded Student UML Diagram",
+                        use_container_width=True,
+                    )
+                    st.write("")
+                    plantuml_student_solution = extract_plantuml_code(
+                        client_openai, uploaded_student_solution, model_choice_step1, prompts
+                    )
+
+            st.write("Extracted PlantUML Code")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.text_area(
+                    "PlantUML Code for Instructor Solution",
+                    plantuml_instructor_solution,
+                    height=600,
+                )
+            with col2:
+                st.text_area(
+                    "PlantUML Code for Student Solution",
+                    plantuml_student_solution,
+                    height=600,
+                )
+
+            st.subheader("Step 2: UML Diagram Comparison")
+            with st.spinner("Comparing instructor and student UML diagrams..."):
+                differences = compare_plantuml(
+                    client_openai,
+                    client_hf_mistral,
+                    client_hf_llama,
+                    plantuml_instructor_solution,
+                    plantuml_student_solution,
+                    model_choice_step2,
+                    prompts
+                )
+                with st.expander("View differences"):
+                    for difference in differences.split("\n"):
+                        st.write(difference)
+
+            st.subheader("Step 3: Structured Feedback")
+            with st.spinner("Preparing structured feedback..."):
+                student_feedback = generate_student_feedback(client_openai, client_hf_mistral, client_hf_llama, differences, model_choice_step3, prompts)
+                educator_feedback = generate_educator_feedback(client_openai, client_hf_mistral, client_hf_llama, differences, model_choice_step3, prompts)
+
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.write("Student Feedback")
+                    st.markdown(f"{student_feedback}")
+                with col2:
+                    st.write("Educator Feedback")
+                    st.markdown(f"{educator_feedback}")
+
+        except Exception as e:
+            st.error(f"Error: {e}")
+else:
+    if not openai_api_key:
+        st.error("Please provide a valid OpenAI API key.")
+    else:
+        st.error("Please provide a valid Hugging Face API key.")