updated the app

app.py

@@ -1,55 +1,81 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import login, HfApi
+from huggingface_hub import login
 import os
 from dotenv import load_dotenv
-from flask import flash, redirect, url_for, session
+import logging
+import sys  # Ensure sys is imported
 
 # Load environment variables
 load_dotenv()
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', stream=sys.stdout)
 
-# Login to Hugging Face (you'll need to set HUGGINGFACE_TOKEN in your Secrets)
-def login():
-    # Get the Hugging Face token from environment variable
-    hf_token = os.getenv('HUGGING_FACE_TOKEN')
-    
-    if not hf_token:
-        flash('Hugging Face token not found. Please set the HUGGING_FACE_TOKEN environment variable.', 'error')
-        return redirect(url_for('index'))
-
-    # Use the token for authentication
-    try:
-        api = HfApi()
-        user = api.whoami(token=hf_token)
-        if user:
-            session['logged_in'] = True
-            flash('Successfully authenticated with Hugging Face', 'success')
-        else:
-            session['logged_in'] = False
-            flash('Failed to authenticate with Hugging Face', 'error')
-    except Exception as e:
-        session['logged_in'] = False
-        flash(f'Error during Hugging Face authentication: {str(e)}', 'error')
-        return redirect(url_for('index'))
-
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-70B")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70B")
-
-def generate_email(context):
-    prompt = f"Generate a professional email based on the following context: {context}"
+# Login to Hugging Face using the token
+hf_token = os.getenv('HUGGING_FACE_TOKEN')
+if hf_token:
+    #login(token=hf_token)
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = hf_token
+else:
+    raise ValueError("HUGGING_FACE_TOKEN environment variable not set.")
+
+# Load the Llama-3.1-8B model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
+
+# Function to generate a formatted email
+def generate_email(recipient_name, recipient_email, industry, recipient_role, details):
+    prompt = (
+        f"Write a cold outreach email for a {recipient_role} "
+        f"working in the {industry} industry. Use the following details: {details}. "
+        "The email should be professional and engaging, without any additional instructions or templates."
+    )
     inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=300)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    outputs = model.generate(**inputs, max_length=300, num_return_sequences=1, temperature=0.7)
+    email_body = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract only the relevant email content
+    email_lines = email_body.split('\n')
+    relevant_lines = []
+    for line in email_lines:
+        if line.strip().lower().startswith(('dear', 'hello', 'hi')):
+            relevant_lines = [line]
+        elif relevant_lines:
+            if line.strip().lower().startswith(('sincerely', 'best regards', 'regards', 'thank you')):
+                break
+            relevant_lines.append(line)
+
+    cleaned_email_body = '\n'.join(relevant_lines).strip()
+
+    # Format the email
+    formatted_email = f"""\
+To: {recipient_name} <{recipient_email}>
+Subject: Collaboration Opportunity
+
+{cleaned_email_body}
+
+Best regards,
+Jane Smith
+Android Developer
+Albertsons
+[Your Contact Information]
+"""
+    return formatted_email
 
 # Create Gradio interface
 iface = gr.Interface(
     fn=generate_email,
-    inputs=gr.Textbox(lines=5, label="Context"),
-    outputs=gr.Textbox(label="Generated Email"),
-    title="Email Generator",
-    description="Enter the context, and the app will generate an email using Llama 3.1 70B."
+    inputs=[
+        gr.Textbox(lines=1, label="Recipient Name"),
+        gr.Textbox(lines=1, label="Recipient Email"),
+        gr.Textbox(lines=1, label="Industry (e.g., Technology, Healthcare)"),
+        gr.Textbox(lines=1, label="Recipient Role (e.g., Manager, Director)"),
+        gr.Textbox(lines=5, label="Personal/Company Details (e.g., name, product)"),
+    ],
+    outputs="text",
+    title="EmailGenie: AI-Powered Email Generator",
+    description="Automate the creation of personalized emails to increase engagement and conversion rates. Enter details to generate tailored emails."
 )
 
 # Launch the app
-iface.launch()
+if __name__ == '__main__':
+    iface.launch()

requirements.txt

@@ -1,4 +1,5 @@
-gradio
-transformers
-torch
-python-dotenv
+gradio==3.50.2
+transformers==4.36.2
+torch==2.1.2
+huggingface_hub==0.20.2
+python-dotenv==1.0.0