Update app.py

app.py

@@ -1,116 +1,90 @@
-# app.py
-
 import streamlit as st
-import requests
-from fpdf import FPDF
 import pytesseract
 from PIL import Image
 import fitz  # PyMuPDF
 import io
-import re
+import requests
 
-# ====== CONFIG ======
-API_KEY = "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"
+# --- Config ---
+API_KEY = "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"  
+API_URL = "https://openrouter.ai/api/v1/chat/completions"
 MODEL = "mistralai/mistral-7b-instruct"
 
-# ====== 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}
-        ]
-    }
-    response = requests.post(url, headers=headers, json=data)
-    if response.status_code == 200:
-        return response.json()['choices'][0]['message']['content']
-    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:
-    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.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 📘
+""")
 
-        st.subheader("📄 Extracted Text")
-        st.text_area("Here is what we found:", value=text_content, height=250)
+# --- Upload ---
+uploaded_file = st.file_uploader("📸 Upload Your Lab Report Image or PDF", type=["jpg", "jpeg", "png", "pdf"])
 
-        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}")
+lab_text = ""
+if uploaded_file:
+    file_bytes = uploaded_file.read()
+    file_ext = uploaded_file.name.split(".")[-1].lower()
 
-        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}")
+    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']
 
-else:
-    st.info("Please upload a lab report image or PDF to get started.")
+    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)