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| import streamlit as st | |
| import pytesseract | |
| from PIL import Image | |
| import fitz # PyMuPDF | |
| import io | |
| import requests | |
| # --- Config --- | |
| API_KEY = "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" | |
| API_URL = "https://openrouter.ai/api/v1/chat/completions" | |
| MODEL = "mistralai/mistral-7b-instruct" | |
| st.set_page_config(page_title="🧪 AI Science Lab Assistant", layout="centered") | |
| st.title("🧪 AI Science Lab Assistant") | |
| st.markdown(""" | |
| This tool helps students evaluate their lab reports by: | |
| - Extracting text from uploaded **PDFs** or **images** | |
| - Automatically **checking for completeness** (Objective, Hypothesis, etc.) | |
| - Giving **improvement tips** 💡 | |
| - Letting you **ask questions** about your report 📘 | |
| """) | |
| # --- Upload --- | |
| uploaded_file = st.file_uploader("📸 Upload Your Lab Report Image or PDF", type=["jpg", "jpeg", "png", "pdf"]) | |
| lab_text = "" | |
| if uploaded_file: | |
| file_bytes = uploaded_file.read() | |
| file_ext = uploaded_file.name.split(".")[-1].lower() | |
| if file_ext == "pdf": | |
| doc = fitz.open(stream=file_bytes, filetype="pdf") | |
| for page in doc: | |
| lab_text += page.get_text() | |
| else: | |
| image = Image.open(io.BytesIO(file_bytes)) | |
| lab_text = pytesseract.image_to_string(image) | |
| st.subheader("📄 Extracted Lab Report Text:") | |
| st.text_area("", lab_text, height=300) | |
| # -- AI Evaluation Prompt -- | |
| full_prompt = f"""Here is a science lab report: | |
| {lab_text} | |
| Please evaluate the report based on the following: | |
| - Does it include sections like Objective, Hypothesis, Procedure, Observation, and Conclusion? | |
| - Point out any missing or incomplete sections. | |
| - Give feedback or improvement suggestions like: "Try writing a more detailed observation." | |
| - Grade it roughly on a scale from 1 to 10 for completeness. | |
| Respond clearly: | |
| """ | |
| def query_ai(prompt): | |
| headers = { | |
| "Authorization": f"Bearer {API_KEY}", | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "model": MODEL, | |
| "messages": [ | |
| {"role": "system", "content": "You are a helpful science teacher."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| } | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| return response.json()['choices'][0]['message']['content'] | |
| if st.button("🧠 Evaluate My Lab Report"): | |
| with st.spinner("Analyzing report with AI..."): | |
| result = query_ai(full_prompt) | |
| st.success("✅ Evaluation Complete") | |
| st.markdown("### 📊 AI Feedback:") | |
| st.markdown(result) | |
| # Ask follow-up questions | |
| st.subheader("🤔 Ask AI About Your Report") | |
| user_question = st.text_input("Type your question") | |
| if st.button("🔍 Ask") and user_question: | |
| with st.spinner("Thinking..."): | |
| followup_prompt = f"Here is the lab report: | |
| {lab_text} | |
| Now answer this question about it: | |
| {user_question}" | |
| followup_response = query_ai(followup_prompt) | |
| st.markdown("### 💬 AI Answer:") | |
| st.markdown(followup_response) | |