Rename streamlit_app.py to app.py

app.py

@@ -0,0 +1,84 @@
+import gradio as gr
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+import pickle
+import requests
+from collections import defaultdict
+import random
+import os
+
+# GitHub-dan label_encoder yükləmək
+def download_label_encoder():
+    url = "https://github.com/AxundovReyal/nlp-disease/raw/main/label_encoder.pkl"
+    headers = {}
+    github_token = os.getenv("GITHUB_TOKEN")
+    if github_token:
+        headers["Authorization"] = f"token {github_token}"
+
+    response = requests.get(url, headers=headers)
+    if response.status_code == 200:
+        with open("label_encoder.pkl", "wb") as f:
+            f.write(response.content)
+        print("label_encoder.pkl faylı uğurla yükləndi.")
+    else:
+        raise Exception(f"Fayl yüklənə bilmədi, error kodu: {response.status_code}")
+
+# Model və label_encoder yüklənməsi
+def load_model():
+    download_label_encoder()
+    with open("label_encoder.pkl", "rb") as f:
+        label_encoder = pickle.load(f)
+    
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = BertForSequenceClassification.from_pretrained(
+        'bert-base-uncased', 
+        num_labels=len(label_encoder.classes_)
+    )
+    model.eval()
+    return tokenizer, model, label_encoder
+
+tokenizer, model, label_encoder = load_model()
+
+# Prediction funksiyası
+def predict_disease(text):
+    if not text.strip():
+        return "Please enter some symptoms!"
+
+    symptoms = [s.strip() for s in text.split(",") if s.strip()]
+    if not symptoms:
+        return "Please enter valid symptoms separated by commas!"
+
+    agg_probs = defaultdict(float)
+    n_shuffles = 10
+    for _ in range(n_shuffles):
+        random.shuffle(symptoms)
+        shuffled_text = ", ".join(symptoms)
+        inputs = tokenizer(shuffled_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze()
+        for i, p in enumerate(probs):
+            agg_probs[i] += p.item()
+    
+    for k in agg_probs:
+        agg_probs[k] /= n_shuffles
+
+    top_3 = sorted(agg_probs.items(), key=lambda x: x[1], reverse=True)[:3]
+    results = []
+    for idx, prob in top_3:
+        label = label_encoder.classes_[idx]
+        results.append(f"{label} — Probability: {prob*100:.2f}%")
+    return "\n".join(results)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_disease,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your symptoms separated by commas"),
+    outputs=gr.Textbox(),
+    title="Disease NLP Classifier",
+    description="Enter your symptoms (comma separated) and get top 3 predicted diseases."
+)
+
+# Launch
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))

streamlit_app.py

@@ -1,87 +0,0 @@
-import os
-import sys
-import streamlit as st
-from transformers import BertTokenizer, BertForSequenceClassification  # Burada BertTokenizer istifadə edirik
-import torch
-import pickle
-import random
-from collections import defaultdict
-import requests
-
-# GitHub-dan fayl yükləmək üçün funksiyanın təyin edilməsi
-def download_label_encoder():
-    url = "https://github.com/AxundovReyal/nlp-disease/raw/main/label_encoder.pkl"
-    headers = {
-        "Authorization": f"token {os.getenv('GITHUB_TOKEN')}"  # GitHub personal access token mühit dəyişəni olaraq qeyd olunmalı
-    }
-    response = requests.get(url, headers=headers)
-
-    if response.status_code == 200:
-        with open("label_encoder.pkl", "wb") as f:
-            f.write(response.content)
-        print("label_encoder.pkl faylı uğurla yükləndi.")
-    else:
-        raise Exception(f"Fayl yüklənə bilmədi, error kodu: {response.status_code}")
-
-# Modelin və label_encoder-in yüklənməsi
-@st.cache_resource
-def load_model():
-    # GitHub-dan label_encoder yükləmək
-    download_label_encoder()
-
-    # Label encoder yüklənməsi əvvəlcə edilir
-    with open("label_encoder.pkl", "rb") as f:
-        label_encoder = pickle.load(f)
-    
-    # Burada AutoTokenizer əvəzinə BertTokenizer istifadə edirik
-    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')  # BERT Tokenizer
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=len(label_encoder.classes_))  # BERT Model
-
-    model.eval()
-
-    return tokenizer, model, label_encoder
-
-tokenizer, model, label_encoder = load_model()
-
-st.title("Disease NLP Classifier")
-
-text = st.text_area("Enter your symptoms separated by commas (e.g. fever, cough, headache):")
-
-def predict(text_input):
-    inputs = tokenizer(text_input, return_tensors="pt", truncation=True, padding=True, max_length=128)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze()
-    return probs
-
-if st.button("Predict"):
-    if not text.strip():
-        st.warning("Please enter some symptoms!")
-    else:
-        symptoms = [s.strip() for s in text.split(",") if s.strip()]
-        if not symptoms:
-            st.warning("Please enter valid symptoms separated by commas!")
-        else:
-            agg_probs = defaultdict(float)
-            n_shuffles = 10
-            for _ in range(n_shuffles):
-                random.shuffle(symptoms)
-                shuffled_text = ", ".join(symptoms)
-                probs = predict(shuffled_text)
-                for i, p in enumerate(probs):
-                    agg_probs[i] += p.item()
-            for k in agg_probs:
-                agg_probs[k] /= n_shuffles
-            top_3 = sorted(agg_probs.items(), key=lambda x: x[1], reverse=True)[:3]
-
-            st.subheader("Top 3 Predicted Diseases (averaged over shuffled inputs):")
-            for idx, prob in top_3:
-                label = label_encoder.classes_[idx]  # Etiketləri doğru alırıq
-                st.write(f"**{label}** — Probability: `{prob * 100:.2f}%`")
-
-# Render port düzəlişi
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 8501))
-    sys.argv = ["streamlit", "run", "streamlit_app.py", f"--server.port={port}", "--server.address=0.0.0.0"]
-    from streamlit.web.cli import main
-    sys.exit(main())