Update from GitHub Actions

README.md

@@ -1,12 +1 @@
----
-title: Chigas
-emoji: 🐠
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+https://huggingface.co/spaces/Hachiru/chigas

hf_space/.github/workflows/main.yml

@@ -18,7 +18,7 @@ jobs:
           
       - name: Install dependencies
         run: |
-          pip install huggingface_hub          
+          pip install huggingface_hub 
           
       - name: Log in to Hugging Face
         run: |

hf_space/app.py

@@ -1,16 +1,17 @@
 import torch
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, AutoModelForTokenClassification, AutoTokenizer, pipeline
 import gradio as gr
-import scispacy
-import spacy
 
 # Load GPT-2 model and tokenizer
 MODEL_NAME = "gpt2"
 tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
 model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)
 
-# Load SciSpacy model for medical NLP
-nlp = spacy.load("en_core_sci_md")
+# Load BioBERT for medical NLP without requiring pip install
+BIOBERT_MODEL = "dmis-lab/biobert-base-cased-v1.1"
+biobert_tokenizer = AutoTokenizer.from_pretrained(BIOBERT_MODEL)
+biobert_model = AutoModelForTokenClassification.from_pretrained(BIOBERT_MODEL)
+nlp_pipeline = pipeline("ner", model=biobert_model, tokenizer=biobert_tokenizer)
 
 # Conversation memory
 conversation_history = []
@@ -44,11 +45,11 @@ def generate_text(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.9,
         for word in stopwords.split(","):
             text = text.replace(word.strip(), "")
         
-        # Extract medical entities
+        # Extract medical entities using BioBERT
         if extract_entities:
-            doc = nlp(text)
-            entities = [(ent.text, ent.label_) for ent in doc.ents]
-            text += "\n\nExtracted Medical Entities:\n" + "\n".join([f"{e[0]} ({e[1]})" for e in entities])
+            entities = nlp_pipeline(text)
+            extracted_entities = set([entity["word"] for entity in entities])
+            text += "\n\nExtracted Medical Entities:\n" + "\n".join(extracted_entities)
         
         responses.append(text)
     

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,18 +1,81 @@
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import gradio as gr
-from transformers import pipeline
+import scispacy
+import spacy
 
-generator = pipeline('text-generation', model='gpt2')
+# Load GPT-2 model and tokenizer
+MODEL_NAME = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
+model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)
 
-def generate_text(prompt, max_length):
-    generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)
-    return generated_text[0]['generated_text']
+# Load SciSpacy model for medical NLP
+nlp = spacy.load("en_core_sci_md")
 
-iface = gr.Interface(
+# Conversation memory
+conversation_history = []
+
+# Function to generate text with medical NLP support
+def generate_text(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.9, clean_output=False, stopwords="", num_responses=1, extract_entities=False):
+    global conversation_history
+    
+    # Combine conversation history with new prompt
+    full_prompt = "\n".join(conversation_history + [prompt])
+    inputs = tokenizer(full_prompt, return_tensors="pt")
+    
+    responses = []
+    for _ in range(num_responses):
+        with torch.no_grad():
+            output = model.generate(
+                **inputs,
+                max_length=max_length,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                do_sample=True
+            )
+        text = tokenizer.decode(output[0], skip_special_tokens=True)
+        
+        # Text cleaning
+        if clean_output:
+            text = text.replace("\n", " ").strip()
+        
+        # Stopword filtering
+        for word in stopwords.split(","):
+            text = text.replace(word.strip(), "")
+        
+        # Extract medical entities
+        if extract_entities:
+            doc = nlp(text)
+            entities = [(ent.text, ent.label_) for ent in doc.ents]
+            text += "\n\nExtracted Medical Entities:\n" + "\n".join([f"{e[0]} ({e[1]})" for e in entities])
+        
+        responses.append(text)
+    
+    # Update conversation history
+    conversation_history.append(prompt)
+    conversation_history.append(responses[0])  # Store only the first response
+    
+    return "\n\n".join(responses)
+
+# Gradio Interface
+demo = gr.Interface(
     fn=generate_text,
-    inputs=[gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"), gr.Slider(minimum=10, maximum=1000, value=50, label="Độ dài văn bản")],
-    outputs="text",
-    title="Ứng dụng Generative AI",
-    description="Tạo văn bản",
+    inputs=[
+        gr.Textbox(label="Medical Query"),
+        gr.Slider(50, 500, step=10, label="Max Length"),
+        gr.Slider(0.1, 1.5, step=0.1, label="Temperature"),
+        gr.Slider(0, 100, step=5, label="Top-K"),
+        gr.Slider(0.0, 1.0, step=0.1, label="Top-P"),
+        gr.Checkbox(label="Clean Output"),
+        gr.Textbox(label="Stopwords (comma-separated)"),
+        gr.Slider(1, 5, step=1, label="Number of Responses"),
+        gr.Checkbox(label="Extract Medical Entities")
+    ],
+    outputs=gr.Textbox(label="Generated Medical Text"),
+    title="Medical AI Assistant",
+    description="Enter a medical-related prompt and adjust parameters to generate AI-assisted text. Supports entity recognition for medical terms.",
 )
 
-iface.launch(share=True) # share=True để triển khai lên Spaces
+# Launch the app
+demo.launch()

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 
-generator = pipeline('text-generation', model='deepseek-ai/DeepSeek-V2-Lite')
+generator = pipeline('text-generation', model='gpt2')
 
 def generate_text(prompt, max_length):
     generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)
@@ -12,7 +12,7 @@ iface = gr.Interface(
     inputs=[gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"), gr.Slider(minimum=10, maximum=1000, value=50, label="Độ dài văn bản")],
     outputs="text",
     title="Ứng dụng Generative AI",
-    description="Tạo văn bản với Llama3",
+    description="Tạo văn bản",
 )
 
 iface.launch(share=True) # share=True để triển khai lên Spaces

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 
-generator = pipeline('text-generation', model='meta-llama/Meta-Llama-3-8B')
+generator = pipeline('text-generation', model='deepseek-ai/DeepSeek-V2-Lite')
 
 def generate_text(prompt, max_length):
     generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/main.yml

@@ -15,7 +15,15 @@ jobs:
         run: |
           git config --global user.email "[email protected]"
           git config --global user.name "Hachiru"
-
+          
+      - name: Install dependencies
+        run: |
+          pip install huggingface_hub          
+          
+      - name: Log in to Hugging Face
+        run: |
+          huggingface-cli login --token ${{ secrets.HF_TOKEN }}
+          
       - name: Push to Hugging Face Spaces
         run: |
           git clone https://huggingface.co/spaces/${{ secrets.HF_USERNAME }}/${{ github.event.repository.name }} hf_space

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 
-generator = pipeline('text-generation', model='gpt2')
+generator = pipeline('text-generation', model='meta-llama/Meta-Llama-3-8B')
 
 def generate_text(prompt, max_length):
     generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)
@@ -12,7 +12,7 @@ iface = gr.Interface(
     inputs=[gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"), gr.Slider(minimum=10, maximum=1000, value=50, label="Độ dài văn bản")],
     outputs="text",
     title="Ứng dụng Generative AI",
-    description="Tạo văn bản với GPT-2",
+    description="Tạo văn bản với Llama3",
 )
 
 iface.launch(share=True) # share=True để triển khai lên Spaces

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 
-generator = pipeline('text-generation', model='meta-llama/Meta-Llama-3-8B')
+generator = pipeline('text-generation', model='gpt2')
 
 def generate_text(prompt, max_length):
     generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)
@@ -9,7 +9,7 @@ def generate_text(prompt, max_length):
 
 iface = gr.Interface(
     fn=generate_text,
-    inputs=[gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"), gr.Slider(minimum=10, maximum=1000, value=50, label="Max Length")],
+    inputs=[gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"), gr.Slider(minimum=10, maximum=1000, value=50, label="Độ dài văn bản")],
     outputs="text",
     title="Ứng dụng Generative AI",
     description="Tạo văn bản với GPT-2",

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -1,18 +1,18 @@
 import gradio as gr
 from transformers import pipeline
 
-generator = pipeline('text-generation', model='gpt2')
+generator = pipeline('text-generation', model='meta-llama/Meta-Llama-3-8B')
 
-def generate_text(prompt):
-    generated_text = generator(prompt, max_length=50, num_return_sequences=1)
+def generate_text(prompt, max_length):
+    generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)
     return generated_text[0]['generated_text']
 
 iface = gr.Interface(
     fn=generate_text,
-    inputs=gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"),
+    inputs=[gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"), gr.Slider(minimum=10, maximum=1000, value=50, label="Max Length")],
     outputs="text",
     title="Ứng dụng Generative AI",
     description="Tạo văn bản với GPT-2",
 )
 
-iface.launch(share=True) # share=True để triển khai lên Spaces
+iface.launch(share=True) # share=True để triển khai lên Spaces

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.github/workflows/main.yml

@@ -0,0 +1,26 @@
+name: Deploy to Hugging Face Spaces
+
+on:
+  push:
+    branches: [ main ]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Setup Git
+        run: |
+          git config --global user.email "[email protected]"
+          git config --global user.name "Hachiru"
+
+      - name: Push to Hugging Face Spaces
+        run: |
+          git clone https://huggingface.co/spaces/${{ secrets.HF_USERNAME }}/${{ github.event.repository.name }} hf_space
+          rsync -av --exclude='.git' ./ hf_space/
+          cd hf_space
+          git add .
+          git commit -m "Update from GitHub Actions"
+          git push https://${{ secrets.HF_USERNAME }}:${{ secrets.HF_TOKEN }}@huggingface.co/spaces/${{ secrets.HF_USERNAME }}/${{ github.event.repository.name }} main

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/app.py

@@ -0,0 +1,18 @@
+import gradio as gr
+from transformers import pipeline
+
+generator = pipeline('text-generation', model='gpt2')
+
+def generate_text(prompt):
+    generated_text = generator(prompt, max_length=50, num_return_sequences=1)
+    return generated_text[0]['generated_text']
+
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=2, placeholder="Nhập văn bản đầu vào"),
+    outputs="text",
+    title="Ứng dụng Generative AI",
+    description="Tạo văn bản với GPT-2",
+)
+
+iface.launch(share=True) # share=True để triển khai lên Spaces

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/README.md

@@ -0,0 +1,12 @@
+---
+title: Chigas
+emoji: 🐠
+colorFrom: pink
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.20.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt

@@ -0,0 +1,4 @@
+pytorch
+tensorflow
+transformers
+gradio

hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt

@@ -1,4 +1,5 @@
-pytorch
+torch
 tensorflow
+tf-keras
 transformers
 gradio

hf_space/hf_space/hf_space/hf_space/hf_space/requirements.txt

@@ -3,3 +3,5 @@ tensorflow
 tf-keras
 transformers
 gradio
+spacy
+scispacy

hf_space/requirements.txt

@@ -3,5 +3,3 @@ tensorflow
 tf-keras
 transformers
 gradio
-spacy
-scispacy