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nlp/added_tokens.json

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+{
+  "[PAD]": 50257
+}

nlp/config.json

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+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 50256,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "use_cache": true,
+  "vocab_size": 50257
+}

nlp/generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "pad_token_id": 50256,
+  "transformers_version": "4.35.2"
+}

nlp/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2753b3af3420afcfd85544b4d4c78427fe4cbc4d9d18823e40dc763accc199d3
+size 497774208

nlp/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

nlp/tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50257": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

requirements.txt

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+streamlit
+transformers
+time

streamlit1.py

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+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import time
+
+# Load the fine-tuned model
+model_path = r"C:\Users\Kush\Desktop\dfuchatbot\nlp"
+tokenizer = GPT2Tokenizer.from_pretrained(model_path)
+model = GPT2LMHeadModel.from_pretrained(model_path)
+
+# Set page title and favicon
+st.set_page_config(page_title="Diabetic Foot Ulcer Chatbot", page_icon=":hospital:")
+
+# Set app title and description
+st.title("Diabetic Foot Ulcer Chatbot")
+st.markdown("Welcome to the Diabetic Foot Ulcer Chatbot. Ask any questions related to diabetic foot ulcers!")
+
+# Define conversation loop
+def chatbot(user_input):
+    # Check for exit command
+    if user_input.lower() == 'exit':
+        st.info("Chat ended. Goodbye!")
+        return
+
+    # Display typing animation
+    with st.spinner(text="Chatbot is typing..."):
+        time.sleep(2)  # Simulate typing time
+
+        # Get model response
+        response = generate_response(user_input)
+
+    # Display model response
+    st.text_area("Chatbot:", value=response, height=100, max_chars=500)
+
+# Generate response from the model
+def generate_response(user_input):
+    # Encode user input
+    input_ids = tokenizer.encode(user_input, return_tensors="pt")
+
+    # Generate model response
+    output = model.generate(
+        input_ids,
+        max_length=100,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        top_k=50,
+        top_p=0.95,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id,
+        early_stopping=True
+    )
+
+    # Decode and return model response
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Get user input
+user_input = st.text_input("You:")
+
+# Start conversation when user submits input
+if st.button("Send"):
+    chatbot(user_input)