Create app.py

app.py

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+## Deploying on HuggingFace
+import streamlit as st
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+from peft import PeftModel, PeftConfig
+import io
+
+st.set_page_config(page_title="AnthroBot", page_icon="🤖", layout="centered")
+
+# Load model & tokenizer
+@st.cache_resource
+def load_model():
+    peft_config = PeftConfig.from_pretrained("SallySims/AnthroBot_Model_Lora")
+    base_model = AutoModelForCausalLM.from_pretrained(
+        peft_config.base_model_name_or_path,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    model = PeftModel.from_pretrained(base_model, "SallySims/AnthroBot_Model_Lora")
+    model.eval()
+
+    tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
+    tokenizer.pad_token = tokenizer.eos_token
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Prediction function
+def get_prediction(prompt):
+    messages = [{"role": "user", "content": prompt}]
+    inputs = tokenizer.apply_chat_template(
+        messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
+    ).to("cuda")
+    output = model.generate(inputs, max_new_tokens=150, temperature=0.7, top_p=0.95)
+    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+    return decoded.split("###")[-1].strip()
+
+# UI Header
+st.title("🧠 AnthroBot")
+st.write("Enter your anthropometric estimates to receive an interpreted summary inputs — manually or via CSV upload.")
+
+# Tabs for input method
+tab1, tab2 = st.tabs(["🧍 Manual Input", "📄 CSV Upload"])
+
+with tab1:
+    st.subheader("Manual Entry")
+    age = st.number_input("Age", 0, 100, 30)
+    sex = st.selectbox("Sex", ["male", "female"])
+    height = st.number_input("Height (cm)", 100.0, 250.0, 150.5)
+    weight = st.number_input("Weight (kg)", 30.0, 200.0, 75.3)
+    wc = st.number_input("Waist Circumference (cm)", 30.0, 150.0, 68.0)
+
+    if st.button("Get Prediction"):
+        prompt = f"Age: {age}, Sex: {sex}, Height: {height} cm, Weight: {weight} kg, WC: {wc} cm\n\n###"
+        prediction = get_prediction(prompt)
+        st.success("Prediction:")
+        st.write(prediction)
+
+with tab2:
+    st.subheader("Batch Upload via CSV")
+    sample_csv = pd.DataFrame({
+        "Age": [30],
+        "Sex": ["female"],
+        "Height": [150.5],
+        "Weight": [75.3],
+        "WC": [68.0]
+    })
+
+    st.download_button("📥 Download Sample CSV", sample_csv.to_csv(index=False), file_name="sample_input.csv")
+
+    uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+
+    if uploaded_file:
+        df = pd.read_csv(uploaded_file)
+        if not all(col in df.columns for col in ["Age", "Sex", "Height", "Weight", "WC"]):
+            st.error("CSV must contain columns: Age, Sex, Height, Weight, WC")
+        else:
+            outputs = []
+            with st.spinner("Generating predictions..."):
+                for _, row in df.iterrows():
+                    prompt = (
+                        f"Age: {row['Age']}, Sex: {row['Sex']}, Height: {row['Height']} cm, "
+                        f"Weight: {row['Weight']} kg, WC: {row['WC']} cm\n\n###"
+                    )
+                    prediction = get_prediction(prompt)
+                    outputs.append(prediction)
+
+                df["Prediction"] = outputs
+                st.success("Here are your predictions:")
+                st.dataframe(df)
+
+                csv_output = df.to_csv(index=False).encode("utf-8")
+                st.download_button("📤 Download Predictions", data=csv_output, file_name="predictions.csv")
+
+