Update README.md

README.md

@@ -10,4 +10,209 @@ pinned: false
 short_description: TweetSentimnet
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Here's a **complete documentation** for deploying your **fine-tuned sentiment analysis model** using **Hugging Face Spaces and Gradio**.  
+
+---
+
+# **Fine-Tuned Sentiment Analysis Deployment Guide**  
+
+This guide explains how to **fine-tune, save, upload, and deploy** a sentiment analysis model using **Hugging Face Transformers, Gradio, and Hugging Face Spaces**.  
+
+---
+
+## **1. Prerequisites**  
+Before proceeding, ensure you have the following installed:  
+
+### **Install Required Libraries**
+```bash
+pip install gradio transformers torch scipy numpy
+```
+
+If you're using **TensorFlow-based models**, also install:
+```bash
+pip install tensorflow
+```
+
+### **Hugging Face Authentication**
+Login to Hugging Face CLI:
+```bash
+huggingface-cli login
+```
+(You'll need an **access token** from [Hugging Face](https://huggingface.co/settings/tokens).)
+
+---
+
+## **2. Fine-Tune Your Sentiment Analysis Model**
+### **Training a Custom Sentiment Model**
+If you haven't already fine-tuned a model, you can do so using `Trainer` from Hugging Face:  
+
+```python
+from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("imdb")  # Example dataset
+
+# Load tokenizer and model
+model_name = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+# Tokenize dataset
+def preprocess(examples):
+    return tokenizer(examples["text"], truncation=True, padding="max_length")
+
+tokenized_datasets = dataset.map(preprocess, batched=True)
+
+# Training Arguments
+training_args = TrainingArguments(
+    output_dir="./fine_tuned_sentiment_model",
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs",
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["test"],
+)
+
+# Train Model
+trainer.train()
+
+# Save Model
+model.save_pretrained("./fine_tuned_sentiment_model")
+tokenizer.save_pretrained("./fine_tuned_sentiment_model")
+```
+
+---
+
+## **3. Upload Model to Hugging Face Hub**
+Once you've fine-tuned your model, upload it to **Hugging Face Model Hub**:  
+
+### **1. Install `huggingface_hub`**
+```bash
+pip install huggingface_hub
+```
+
+### **2. Push Model to Hugging Face**
+```python
+from huggingface_hub import notebook_login
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+notebook_login()  # Authenticate
+
+# Define model name
+repo_name = "your-username/sentiment-analysis-model"
+
+# Load fine-tuned model
+model = AutoModelForSequenceClassification.from_pretrained("./fine_tuned_sentiment_model")
+tokenizer = AutoTokenizer.from_pretrained("./fine_tuned_sentiment_model")
+
+# Push model to Hugging Face Hub
+model.push_to_hub(repo_name)
+tokenizer.push_to_hub(repo_name)
+```
+
+Your fine-tuned model is now available at **https://huggingface.co/your-username/sentiment-analysis-model**.
+
+---
+
+## **4. Deploy Sentiment Model Using Gradio**
+To create a **Gradio-based web interface**, follow these steps:
+
+### **1. Create `app.py`**
+Save the following script as `app.py`:
+
+```python
+import gradio as gr
+import numpy as np
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+from scipy.special import softmax
+
+# Load fine-tuned model from Hugging Face Hub
+MODEL_NAME = "your-username/sentiment-analysis-model"  # Replace with your model repo
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+config = AutoConfig.from_pretrained(MODEL_NAME)
+
+# Preprocess function
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+# Sentiment Prediction Function
+def predict_sentiment(text):
+    text = preprocess(text)
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+
+    # Get sentiment labels and scores
+    ranking = np.argsort(scores)[::-1]
+    result = {config.id2label[ranking[i]]: round(float(scores[ranking[i]]) * 100, 2) for i in range(scores.shape[0])}
+    return result
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text..."),
+    outputs=gr.Label(),
+    title="Fine-Tuned Sentiment Analysis",
+    description="Enter a sentence to analyze its sentiment (Positive, Neutral, Negative).",
+)
+
+# Launch the app
+interface.launch()
+```
+
+---
+
+## **5. Upload to Hugging Face Spaces**
+### **1. Create a Hugging Face Space**
+- Go to [Hugging Face Spaces](https://huggingface.co/spaces).  
+- Click **Create new Space**.  
+- Choose **Gradio** as the SDK.  
+- Set the repository name (e.g., `sentiment-analysis-app`).  
+- Click **Create Space**.  
+
+### **2. Upload Files**
+- Upload `app.py` in the Space repository.
+- Create and upload a `requirements.txt` file with:
+  ```
+  gradio
+  transformers
+  torch
+  scipy
+  numpy
+  ```
+
+### **3. Deploy the Model**
+Once the files are uploaded, Hugging Face will **automatically install dependencies** and **launch the app**. You can access it via the **public URL** provided by Hugging Face.
+
+---
+
+## **6. Testing & Sharing**
+Once deployed, test the model by entering different texts and see the predicted sentiment. Share the **public Hugging Face Space link** with others to let them use it.
+
+---
+
+## **7. Summary**
+### ✅ **Fine-tune a sentiment analysis model**  
+### ✅ **Upload it to Hugging Face Model Hub**  
+### ✅ **Deploy it using Gradio & Hugging Face Spaces**  
+### ✅ **Make it publicly accessible for users**  
+
+🚀 **Your fine-tuned sentiment analysis model is now LIVE!** 🎉