Text inferencing implemented

app.py

@@ -1,9 +1,45 @@
-import numpy as np
+import torch
+import torch.nn as nn
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+class _MLPVectorProjector(nn.Module):
+    def __init__(
+        self, input_hidden_size: int, lm_hidden_size: int, num_layers: int, width: int
+    ):
+        super(_MLPVectorProjector, self).__init__()
+        self.mlps = nn.ModuleList()
+        for _ in range(width):
+            mlp = [nn.Linear(input_hidden_size, lm_hidden_size, bias=False)]
+            for _ in range(1, num_layers):
+                mlp.append(nn.GELU())
+                mlp.append(nn.Linear(lm_hidden_size, lm_hidden_size, bias=False))
+            self.mlps.append(nn.Sequential(*mlp))
+
+    def forward(self, x):
+        return torch.cat([mlp(x) for mlp in self.mlps], dim=-2)
+
+model_name = "microsoft/phi-2"
+
+phi2_text = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
+                                                 torch_dtype = torch.float16)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
 
 def textMode(text, count):
-    return "In progress"
+    count = int(count)
+    inputs = tokenizer(text, return_tensors="pt")
+    prediction = tokenizer.batch_decode(
+    phi2_text.generate(
+        **inputs, 
+        max_new_tokens=500,
+        bos_token_id=tokenizer.bos_token_id, 
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id
+    )
+    return prediction[0].rstrip('<|endoftext|>').rstrip("\n")[:count]
+        
 
 
 def imageMode(image, question):