Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import pandas as pd
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer, AutoModel
+
+# --- Load BLIP model ---
+blip_path = "Models/blip_skin_cancer"
+processor = BlipProcessor.from_pretrained(blip_path)
+blip_model = BlipForConditionalGeneration.from_pretrained(blip_path).to("cpu")
+
+# --- Load BioClinicalBERT + BiLSTM classifier ---
+tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
+bert_model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
+
+class BiLSTMClassifier(nn.Module):
+    def __init__(self, bert_model, hidden_dim=256, num_classes=7, dropout=0.5):
+        super().__init__()
+        self.bert = bert_model
+        self.lstm = nn.LSTM(768, hidden_dim, batch_first=True, bidirectional=True)
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(hidden_dim*2, num_classes)
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        embeddings = outputs.last_hidden_state
+        lstm_out, _ = self.lstm(embeddings)
+        pooled = torch.mean(lstm_out, dim=1)
+        pooled = self.dropout