Update app.py

app.py

@@ -1,28 +1,50 @@
 import os
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import InferenceApi
+from huggingface_hub import InferenceClient
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # Get the token from the environment variable set in HF Spaces
 hf_token = os.environ.get("HUGGINGFACE_TOKEN")
+if not hf_token:
+    logger.error("HUGGINGFACE_TOKEN environment variable is not set")
+    raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
 
 # Model name
 model_name = "meta-llama/Llama-2-7b-chat-hf"
 
-# Create an InferenceApi client
-inference = InferenceApi(repo_id=model_name, token=hf_token)
+# Create an InferenceClient
+client = InferenceClient(model=model_name, token=hf_token)
 
 def generate_text(prompt):
-    response = inference(inputs=prompt, parameters={"max_length": 500})
-    return response[0]['generated_text']
+    try:
+        logger.info(f"Generating text for prompt: {prompt[:50]}...")
+        response = client.text_generation(prompt, max_new_tokens=500, temperature=0.7)
+        return response
+    except Exception as e:
+        logger.error(f"Error in generate_text: {str(e)}")
+        return f"An error occurred: {str(e)}"
 
 def generate_email(industry, recipient_role, company_details):
-    prompt = f"Generate a personalized cold outreach email for a {recipient_role} in the {industry} industry. Company details: {company_details}"
-    return generate_text(prompt)
+    try:
+        logger.info(f"Generating email for {industry}, {recipient_role}")
+        prompt = f"Generate a personalized cold outreach email for a {recipient_role} in the {industry} industry. Company details: {company_details}"
+        return generate_text(prompt)
+    except Exception as e:
+        logger.error(f"Error in generate_email: {str(e)}")
+        return f"An error occurred in generate_email: {str(e)}"
 
 def refine_email(email, tone, context):
-    prompt = f"Refine the following email to match a {tone} tone and consider this context: {context}\n\nEmail: {email}"
-    return generate_text(prompt)
+    try:
+        logger.info(f"Refining email with tone: {tone}")
+        prompt = f"Refine the following email to match a {tone} tone and consider this context: {context}\n\nEmail: {email}"
+        return generate_text(prompt)
+    except Exception as e:
+        logger.error(f"Error in refine_email: {str(e)}")
+        return f"An error occurred in refine_email: {str(e)}"
 
 # Gradio interface
 with gr.Blocks() as demo:
@@ -31,18 +53,18 @@ with gr.Blocks() as demo:
     with gr.Tab("Generate Email"):
         industry = gr.Textbox(label="Industry")
         recipient_role = gr.Textbox(label="Recipient Role")
-        company_details = gr.Textbox(label="Company/Personal Details")
+        company_details = gr.Textbox(label="Company/Personal Details", lines=5)
         generate_button = gr.Button("Generate Email")
-        output = gr.Textbox(label="Generated Email")
+        output = gr.Textbox(label="Generated Email", lines=10)
         
         generate_button.click(generate_email, inputs=[industry, recipient_role, company_details], outputs=output)
     
     with gr.Tab("Refine Email"):
-        email_input = gr.Textbox(label="Input Email")
+        email_input = gr.Textbox(label="Input Email", lines=10)
         tone = gr.Dropdown(["Formal", "Casual", "Enthusiastic"], label="Desired Tone")
-        context = gr.Textbox(label="Additional Context")
+        context = gr.Textbox(label="Additional Context", lines=3)
         refine_button = gr.Button("Refine Email")
-        refined_output = gr.Textbox(label="Refined Email")
+        refined_output = gr.Textbox(label="Refined Email", lines=10)
         
         refine_button.click(refine_email, inputs=[email_input, tone, context], outputs=refined_output)