Create app.py

app.py

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+import transformers
+import gradio as gr
+import torch
+import csv
+
+# Load a pre-trained model
+model = transformers.AutoModel.from_pretrained("bert-base-uncased")
+model.eval()
+
+# Define a function to run the model on input text
+def predict_sentiment(input_text):
+    input_ids = transformers.BertTokenizer.encode(input_text, add_special_tokens=True)
+    input_ids = torch.tensor(input_ids).unsqueeze(0)
+    outputs = model(input_ids)
+    logits = outputs[0]
+    sentiment = "Positive" if logits[0][0] > 0 else "Negative"
+    return sentiment
+
+# Create a chat history to store previous inputs and outputs
+chat_history = []
+
+# Define a function to update the chat history
+def update_history(input_text, sentiment):
+    chat_history.append(f"User: {input_text}")
+    chat_history.append(f"Model: {sentiment}")
+
+# Read the prompts from a CSV file
+prompts = []
+with open("prompts.csv") as csvfile:
+    reader = csv.reader(csvfile)
+    for row in reader:
+        prompts.append(row[0])
+
+# Create an input interface using Gradio
+inputs = gr.inputs.Dropdown(prompts, default=prompts[0])
+
+# Create an output interface using Gradio
+outputs = gr.outputs.Chatbox(label="Sentiment", lines=1)
+
+# Run the interface
+interface = gr.Interface(predict_sentiment, inputs, outputs, title="Sentiment Analysis",
+                         on_output=update_history)
+interface.launch()