Update app.py

app.py

@@ -1,133 +1,64 @@
-import os
-from datetime import datetime
-from PyPDF2 import PdfReader
-from docx import Document
 import streamlit as st
-from groq import Groq
-import textwrap
+import requests
+
+# Hugging Face API setup
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
+headers = {"Authorization": "Bearer YOUR_HF_API_KEY"}
+
+def query_llm(prompt):
+    payload = {"inputs": prompt}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()[0]['generated_text']
+
+# Static templates for known experiments
+experiment_templates = {
+    "Vinegar and Baking Soda": {
+        "goal": "Observe a chemical reaction and gas formation.",
+        "materials": "Vinegar, Baking Soda, Container",
+        "default_hypothesis": "Mixing will cause bubbles or fizz due to gas release.",
+        "result": "Bubbles and fizz from CO2 gas produced in an acid-base reaction.",
+        "explanation": "Acetic acid in vinegar reacts with sodium bicarbonate to form carbon dioxide gas."
+    },
+    "Lemon Battery": {
+        "goal": "Generate electricity using a lemon as a battery.",
+        "materials": "Lemon, Copper coin, Zinc nail, Wires, LED",
+        "default_hypothesis": "The lemon will generate voltage to light up a small LED.",
+        "result": "LED glows slightly due to electron flow.",
+        "explanation": "The citric acid acts as electrolyte between the copper and zinc electrodes."
+    }
+}
+
+# Streamlit UI
+st.set_page_config(page_title="Science Lab Assistant", layout="centered")
+st.title("🧪 Science Lab Assistant")
+
+# Select known or custom experiment
+exp_option = st.selectbox("Choose an experiment", ["Custom"] + list(experiment_templates.keys()))
+
+if exp_option != "Custom":
+    data = experiment_templates[exp_option]
+    st.subheader("📋 Experiment Summary")
+    st.write(f"**Goal:** {data['goal']}")
+    st.write(f"**Materials:** {data['materials']}")
+    st.write(f"**Suggested Hypothesis:** {data['default_hypothesis']}")
+
+    if st.button("🔍 Show Result & Explanation"):
+        st.success(f"**Result:** {data['result']}")
+        st.info(f"**Why?:** {data['explanation']}")
+
+else:
+    st.subheader("🔬 Describe your experiment")
+    experiment_name = st.text_input("Name or short description of your experiment")
+    goal = st.text_area("What is the goal of your experiment?")
+    materials = st.text_area("List the materials involved")
+
+    if st.button("🧠 Generate Hypothesis and Expected Result"):
+        prompt = f"""I am conducting a school science experiment. 
+        Experiment: {experiment_name}
+        Goal: {goal}
+        Materials: {materials}
+        Please suggest a hypothesis and likely result with a brief scientific explanation."""
+        result = query_llm(prompt)
+        st.markdown("### 🤖 Assistant's Suggestion")
+        st.write(result)
 
-# ✅ Page Configuration
-st.set_page_config(page_title="AI Study Plan Assistant", layout="wide")
-
-# ✅ Load Groq Client
-@st.cache_resource
-def load_groq_client():
-    return Groq(api_key=os.getenv("GROQ_API_KEY"))
-
-groq_client = load_groq_client()
-
-# ✅ File Text Extraction
-def extract_text(file):
-    ext = os.path.splitext(file.name)[1].lower()
-    if ext == ".txt":
-        return file.read().decode("utf-8")
-    elif ext == ".docx":
-        doc = Document(file)
-        return "\n".join([para.text for para in doc.paragraphs])
-    elif ext == ".pdf":
-        pdf_reader = PdfReader(file)
-        text = ""
-        for page in pdf_reader.pages:
-            page_text = page.extract_text()
-            if page_text:
-                text += page_text + "\n"
-        return text
-    else:
-        raise ValueError("Only .txt, .docx, and .pdf files are supported.")
-
-# ✅ Chunking helper
-def chunk_text(text, chunk_size=1500):
-    return textwrap.wrap(text, width=chunk_size, break_long_words=False, replace_whitespace=False)
-
-# ✅ Query Groq LLM
-def query_groq(prompt, temperature=0.4):
-    try:
-        chat = groq_client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model="llama3-8b-8192",
-            temperature=temperature
-        )
-        return chat.choices[0].message.content.strip()
-    except Exception as e:
-        return f"⚠️ Groq API Error: {str(e)}"
-
-# ✅ Generate Study Plan with chunking
-def generate_plan(file, hours_per_day, exam_date, language):
-    try:
-        content = extract_text(file)
-        today = datetime.now().date()
-        exam = datetime.strptime(exam_date.strip(), "%Y-%m-%d").date()
-        days = (exam - today).days
-
-        if days <= 0:
-            return "❌ Exam date must be in the future."
-
-        chunks = chunk_text(content, chunk_size=1500)
-        plan_parts = []
-
-        for idx, chunk in enumerate(chunks):
-            prompt = f"""This is part {idx + 1} of {len(chunks)} of a syllabus.
-Create a study plan segment for this syllabus. Total study duration is {days} days with {hours_per_day} hours/day.
-Write the study plan in {language}.
-
-Syllabus:
-\"\"\"
-{chunk}
-\"\"\"
-"""
-            response = query_groq(prompt)
-            plan_parts.append(response)
-
-        return "\n\n".join(plan_parts)
-
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}"
-
-# ✅ Ask Question with context chunking
-def ask_question(file, question):
-    try:
-        context = extract_text(file)
-        chunks = chunk_text(context, chunk_size=1500)
-        answers = []
-
-        for idx, chunk in enumerate(chunks):
-            prompt = f"""Use the following part of study material to answer the question:
-
-Material:
-\"\"\"
-{chunk}
-\"\"\"
-
-Question: {question}
-Answer:"""
-            answer = query_groq(prompt)
-            answers.append(f"Part {idx + 1}:\n{answer}")
-
-        return "\n\n".join(answers)
-
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}"
-
-# ✅ Streamlit UI
-st.sidebar.title("📚 Study Assistant Options")
-uploaded_file = st.sidebar.file_uploader("Upload syllabus (.txt, .docx, .pdf)", type=["txt", "docx", "pdf"])
-study_hours = st.sidebar.number_input("Study hours per day", min_value=1, max_value=12, value=3)
-exam_date = st.sidebar.text_input("Exam Date (YYYY-MM-DD)", value="2025-06-30")
-language = st.sidebar.selectbox("Select Language", ["English", "Urdu"])
-
-st.title("🧠 AI Study Plan & QA Assistant")
-
-tab1, tab2 = st.tabs(["📅 Generate Study Plan", "❓ Ask a Question"])
-
-with tab1:
-    st.subheader("Generate a Personalized Study Plan")
-    if uploaded_file and st.button("Generate Study Plan"):
-        result = generate_plan(uploaded_file, study_hours, exam_date, language)
-        st.text_area("Study Plan", result, height=400)
-
-with tab2:
-    st.subheader("Ask Questions from Uploaded Material")
-    question = st.text_input("Enter your question:")
-    if uploaded_file and question and st.button("Get Answer"):
-        answer = ask_question(uploaded_file, question)
-        st.text_area("Answer", answer, height=300)