Update app.py

app.py

@@ -1,119 +1,116 @@
 # app.py
+
 import streamlit as st
 import requests
-from datetime import datetime
 from fpdf import FPDF
+import pytesseract
+from PIL import Image
+import fitz  # PyMuPDF
+import io
+import re
 
-# Replace with your actual OpenRouter or Hugging Face endpoint and key
-API_URL = "https://openrouter.ai/api/v1/chat/completions"
+# ====== CONFIG ======
 API_KEY = "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"
 MODEL = "mistralai/mistral-7b-instruct"
 
-# ------------------------- AI QUERY FUNCTION -------------------------
-def query_ai(prompt):
+# ====== FUNCTIONS ======
+def query_llm(prompt):
+    url = "https://openrouter.ai/api/v1/chat/completions"
     headers = {
         "Authorization": f"Bearer {API_KEY}",
         "Content-Type": "application/json"
     }
     data = {
         "model": MODEL,
-        "messages": [{"role": "user", "content": prompt}],
-        "temperature": 0.7
+        "messages": [
+            {"role": "user", "content": prompt}
+        ]
     }
-    try:
-        response = requests.post(API_URL, headers=headers, json=data)
-        response.raise_for_status()
+    response = requests.post(url, headers=headers, json=data)
+    if response.status_code == 200:
         return response.json()['choices'][0]['message']['content']
-    except Exception as e:
-        return f"❌ API Error: {str(e)}"
-
-# ---------------------- EXPERIMENT TEMPLATES ------------------------
-experiments = {
-    "Vinegar + Baking Soda": {
-        "hypothesis": "Mixing vinegar and baking soda will produce bubbles due to a chemical reaction.",
-        "concept": "Acid-base reaction producing carbon dioxide."
-    },
-    "Floating Egg": {
-        "hypothesis": "An egg will float in salt water but sink in plain water.",
-        "concept": "Density difference between saltwater and freshwater."
-    },
-    "Lemon Battery": {
-        "hypothesis": "A lemon can produce electricity to power a small LED.",
-        "concept": "Chemical energy conversion to electrical energy."
-    }
-}
-
-# -------------------------- PDF REPORT ------------------------------
-def generate_pdf_report(exp_name, hypo, explanation, result):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, txt="Science Lab Report", ln=True, align='C')
-    pdf.ln(10)
-    pdf.multi_cell(0, 10, txt=f"Experiment: {exp_name}\n\nHypothesis: {hypo}\n\nAI Explanation: {explanation}\n\nExpected Result: {result}")
-    filename = f"report_{datetime.now().strftime('%Y%m%d%H%M%S')}.pdf"
-    pdf.output(filename)
-    return filename
-
-# --------------------------- STREAMLIT UI ---------------------------
-st.set_page_config(page_title="🧪 Science Lab Assistant", layout="centered")
-st.title("🧪 Science Lab Assistant")
-st.markdown("Helping students understand, hypothesize, and explain science experiments.")
-
-exp_choice = st.selectbox("Choose an experiment or enter your own:", list(experiments.keys()) + ["Custom Experiment"])
-
-if exp_choice != "Custom Experiment":
-    default_hypo = experiments[exp_choice]["hypothesis"]
-    concept = experiments[exp_choice]["concept"]
-else:
-    default_hypo = ""
-    concept = ""
-
-with st.form("experiment_form"):
-    exp_name = st.text_input("Experiment Name", value=exp_choice)
-    hypo = st.text_area("Your Hypothesis", value=default_hypo)
-    submit = st.form_submit_button("🔍 Explain Experiment")
-
-if submit:
-    with st.spinner("Thinking like a scientist..."):
-        prompt = f"Explain the following school science experiment clearly for a student.\n\nExperiment: {exp_name}\nHypothesis: {hypo}\nExplain what happens, why it happens, and what the expected result is."
-        explanation = query_ai(prompt)
-
-    st.success("AI Explanation")
-    st.write(explanation)
-
-    # Generate expected result
-    with st.spinner("Predicting result..."):
-        result = query_ai(f"What is the expected result of this experiment: {exp_name}?")
-    st.info(f"**Expected Result:** {result}")
-
-    # Conceptual Science
-    if concept:
-        st.caption(f"🧠 Science Concept: {concept}")
-
-    # PDF Download
-    pdf_file = generate_pdf_report(exp_name, hypo, explanation, result)
-    with open(pdf_file, "rb") as file:
-        st.download_button("📄 Download Lab Report (PDF)", file, file_name=pdf_file)
-
-# --------------------- OPTIONAL FILE UPLOAD -------------------------
-st.markdown("---")
-st.subheader("📸 Upload Your Lab Report Image (optional)")
-uploaded_file = st.file_uploader("Upload an image for feedback", type=["jpg", "png", "jpeg"])
+    else:
+        return f"❌ API Error: {response.text}"
+
+def extract_text_from_image(image):
+    return pytesseract.image_to_string(Image.open(image))
+
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    for page in doc:
+        text += page.get_text()
+    return text
+
+def evaluate_lab_report(text):
+    missing_sections = []
+    suggestions = []
+    score = 0
+    total = 5
+
+    if re.search(r"objective", text, re.I): score += 1
+    else:
+        missing_sections.append("Objective")
+        suggestions.append("Add a clear objective stating what the experiment is trying to discover.")
+
+    if re.search(r"hypothesis", text, re.I): score += 1
+    else:
+        missing_sections.append("Hypothesis")
+        suggestions.append("Include a hypothesis that predicts the experiment outcome.")
+
+    if re.search(r"procedure|method", text, re.I): score += 1
+    else:
+        missing_sections.append("Procedure")
+        suggestions.append("Describe the steps followed during the experiment.")
+
+    if re.search(r"observation", text, re.I): score += 1
+    else:
+        missing_sections.append("Observation")
+        suggestions.append("Mention what was observed during the experiment.")
+
+    if re.search(r"conclusion", text, re.I): score += 1
+    else:
+        missing_sections.append("Conclusion")
+        suggestions.append("Summarize the findings in a conclusion.")
+
+    return score, total, missing_sections, suggestions
+
+# ====== STREAMLIT UI ======
+st.set_page_config(page_title="🧪 AI Science Lab Assistant", layout="centered")
+st.title("🧪 AI Science Lab Assistant")
+st.markdown("Upload your **lab report** (image or PDF), and the AI will analyze, evaluate, and answer questions about it!")
+
+uploaded_file = st.file_uploader("📤 Upload Lab Report (Image or PDF)", type=["png", "jpg", "jpeg", "pdf"])
+
 if uploaded_file:
-    st.image(uploaded_file, caption="Uploaded Report", use_column_width=True)
-    st.markdown("✅ Received. AI feedback coming soon (future feature).")
-
-# ------------------------ GLOSSARY HELPER ---------------------------
-st.markdown("---")
-st.subheader("📘 Ask About a Science Term")
-term = st.text_input("Enter a science term (e.g., osmosis, catalyst)")
-if term:
-    st.write(query_ai(f"Explain the term '{term}' in simple words for a student."))
-
-# ---------------------- FEEDBACK SECTION ----------------------------
-st.markdown("---")
-st.subheader("💬 Feedback")
-user_feedback = st.text_area("What can we improve in this app?")
-if user_feedback:
-    st.success("Thanks! Your feedback was recorded (simulated). 📨")
+    with st.spinner("🔍 Extracting content..."):
+        if uploaded_file.type == "application/pdf":
+            text_content = extract_text_from_pdf(uploaded_file)
+        else:
+            text_content = extract_text_from_image(uploaded_file)
+
+        st.subheader("📄 Extracted Text")
+        st.text_area("Here is what we found:", value=text_content, height=250)
+
+        st.subheader("📊 Report Evaluation")
+        score, total, missing, tips = evaluate_lab_report(text_content)
+        st.markdown(f"✅ **Score**: {score}/{total}")
+        if missing:
+            st.markdown("❗ **Missing Sections**:")
+            for m in missing:
+                st.write(f"- {m}")
+        if tips:
+            st.markdown("💡 **Improvement Tips**:")
+            for t in tips:
+                st.write(f"- {t}")
+
+        st.subheader("💬 Ask a Question About This Report")
+        user_q = st.text_input("Your question:")
+        if user_q:
+            full_prompt = f"Here is a science lab report:
+{text_content}\n\nNow answer this question about it:\n{user_q}"
+            response = query_llm(full_prompt)
+            st.markdown(f"**Answer**: {response}")
+
+else:
+    st.info("Please upload a lab report image or PDF to get started.")