Update app.py

app.py

@@ -1,82 +1,50 @@
 import streamlit as st
-import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np
 from PIL import Image
-
-# Utility functions
-def analyze_curve(temp, weight):
-    temp = np.array(temp)
-    weight = np.array(weight)
-
-    if len(temp) < 5:
-        return None, None, None
-
-    # Normalize if >100
-    if weight.max() > 100:
-        weight = 100 * weight / weight[0]
-
-    # Derivative
-    dy = np.gradient(weight, temp)
-
-    # Onset
-    onset_idx = np.argmax(dy < -0.05)
-    onset_temp = temp[onset_idx] if onset_idx > 0 else None
-
-    # Peak degradation
-    peak_idx = np.argmin(dy)
-    peak_temp = temp[peak_idx] if peak_idx > 0 else None
-
-    # Weight loss
-    weight_loss = weight[0] - weight[-1]
-
-    return onset_temp, peak_temp, weight_loss
-
-
-st.title("TGA Graph Interpreter (Multi-Curve Support)")
-
-st.write("""
-Upload your TGA data in **CSV/Excel** format with columns:
-- First column = Temperature (°C)
-- Other columns = Weight (%) for each thermogram
-""")
-
-uploaded_file = st.file_uploader("Upload CSV or Excel", type=["csv", "xlsx"])
-
-if uploaded_file:
-    # Read data
-    if uploaded_file.name.endswith(".csv"):
-        df = pd.read_csv(uploaded_file)
-    else:
-        df = pd.read_excel(uploaded_file)
-
-    st.write("### Uploaded Data", df.head())
-
-    temp = df.iloc[:, 0]
-    fig, ax = plt.subplots()
-
-    for col in df.columns[1:]:
-        weight = df[col]
-        onset, peak, loss = analyze_curve(temp, weight)
-
-        ax.plot(temp, weight, label=col)
-
-        st.subheader(f"Results for {col}")
-        if onset:
-            st.write(f"- **Onset Temperature:** {onset:.2f} °C")
-        if peak:
-            st.write(f"- **Peak Degradation Temperature:** {peak:.2f} °C")
-        st.write(f"- **Weight Loss:** {loss:.2f} %")
-
-    ax.set_xlabel("Temperature (°C)")
-    ax.set_ylabel("Weight (%)")
-    ax.legend()
-    st.pyplot(fig)
-
-st.write("---")
-st.write("Alternatively, upload a TGA **image (PNG/JPG)** just for visualization (no extraction).")
-
-img_file = st.file_uploader("Upload TGA Image", type=["png", "jpg", "jpeg"])
-if img_file:
-    img = Image.open(img_file)
-    st.image(img, caption="Uploaded TGA Graph", use_column_width=True)
+import io
+
+st.set_page_config(page_title="TGA Graph Interpreter", layout="wide")
+
+st.title("TGA Graph Interpreter")
+st.write("Upload TGA thermograms and record degradation details. The app will generate a structured discussion.")
+
+uploaded_files = st.file_uploader(
+    "Upload thermogram(s)", 
+    type=["png", "jpg", "jpeg", "pdf"], 
+    accept_multiple_files=True
+)
+
+all_discussions = []
+
+if uploaded_files:
+    for i, uploaded_file in enumerate(uploaded_files):
+        st.subheader(f"Thermogram {i+1}: {uploaded_file.name}")
+
+        # Display thermogram
+        file_bytes = uploaded_file.read()
+        image = Image.open(io.BytesIO(file_bytes))
+        st.image(image, use_container_width=True)
+
+        st.markdown("**Enter analysis details:**")
+        onset_temp = st.number_input(f"Onset temperature (°C) for {uploaded_file.name}", min_value=0, step=1)
+        peak_temp = st.number_input(f"Peak degradation temperature (°C) for {uploaded_file.name}", min_value=0, step=1)
+        weight_loss = st.number_input(f"Total weight loss (%) for {uploaded_file.name}", min_value=0.0, step=0.1)
+        residue = st.number_input(f"Residue at 800 °C (%) for {uploaded_file.name}", min_value=0.0, step=0.1)
+
+        if st.button(f"Add Discussion for {uploaded_file.name}"):
+            discussion = (
+                f"**{uploaded_file.name}**:\n"
+                f"- Onset of degradation observed at ~{onset_temp} °C.\n"
+                f"- Major degradation peak at ~{peak_temp} °C.\n"
+                f"- The material shows a total weight loss of ~{weight_loss:.2f}%.\n"
+                f"- Residual mass at 800 °C is ~{residue:.2f}%.\n\n"
+                f"These results indicate thermal stability until {onset_temp} °C, "
+                f"with significant decomposition occurring around {peak_temp} °C. "
+                f"The remaining char (~{residue:.2f}%) suggests potential "
+                f"applications where high-temperature stability is desired."
+            )
+            all_discussions.append(discussion)
+
+if all_discussions:
+    st.header("Discussion")
+    for d in all_discussions:
+        st.markdown(d)