Changed llama model

app.py

@@ -68,58 +68,58 @@ model_sentiment.eval()
 model_sentiment.to(device)
 
 
-model_name_or_path = "TheBloke/Llama-2-13B-chat-GGML"
-model_basename = "llama-2-13b-chat.ggmlv3.q5_1.bin"  # The model is in bin format
+model_name_or_path = "TheBloke/Llama-2-7B-Chat-GGUF"
+model_basename = "llama-2-7b-chat.Q2_K.gguf"  # The model is in bin format
 
 # Download the model file
 print('downloading llama model...')
-# model_path_llama = hf_hub_download(repo_id=model_name_or_path, filename=model_basename, force_download=True, local_dir="./llama_model")
+model_path_llama = hf_hub_download(repo_id=model_name_or_path, filename=model_basename, force_download=True, local_dir="./llama_model")
 print('finished download...')
 # Initialize the Llama model with appropriate settings for GPU
-# lcpp_llm = Llama(
-#     model_path=model_path_llama,
-# )
-
-# def generate_email_response(email_prompt):
-#     # Check input received by the function
-#     print("Received prompt:", email_prompt)
-
-#     # Determine if the input is a shorthand command or an actual email
-#     if 'email to' in email_prompt.lower():
-#         # Assume it's a shorthand command, format appropriately
-#         formatted_prompt = f'''
-#         Email received: "{email_prompt}"
-#         Respond to this email, ensuring a professional tone, providing a concise update, and addressing any potential concerns the sender might have.
-#         Response:
-#         '''
-#     else:
-#         # Assume it's direct email content
-#         formatted_prompt = f'''
-#         Email received: "{email_prompt}"
-#         Respond to this email, ensuring a professional tone, providing a concise update, and addressing any potential concerns the sender might have.
-#         Response:
-#         '''
-
-#     # Generate response using Llama-2 model
-#     try:
-#         response = lcpp_llm(
-#             prompt=formatted_prompt,
-#             max_tokens=256,
-#             temperature=0.5,
-#             top_p=0.95,
-#             repeat_penalty=1.2,
-#             top_k=150,
-#             echo=True
-#         )
-#         generated_response = response["choices"][0]["text"]
-#         # Remove the input part from the output if it is included
-#         if formatted_prompt in generated_response:
-#             generated_response = generated_response.replace(formatted_prompt, '').strip()
-#         print("Generated response:", generated_response)
-#         return generated_response
-#     except Exception as e:
-#         print("Error in response generation:", str(e))
-#         return "Failed to generate response, please check the console for errors."
+lcpp_llm = Llama(
+    model_path=model_path_llama,
+)
+
+def generate_email_response(email_prompt):
+    # Check input received by the function
+    print("Received prompt:", email_prompt)
+
+    # Determine if the input is a shorthand command or an actual email
+    if 'email to' in email_prompt.lower():
+        # Assume it's a shorthand command, format appropriately
+        formatted_prompt = f'''
+        Email received: "{email_prompt}"
+        Respond to this email, ensuring a professional tone, providing a concise update, and addressing any potential concerns the sender might have.
+        Response:
+        '''
+    else:
+        # Assume it's direct email content
+        formatted_prompt = f'''
+        Email received: "{email_prompt}"
+        Respond to this email, ensuring a professional tone, providing a concise update, and addressing any potential concerns the sender might have.
+        Response:
+        '''
+
+    # Generate response using Llama-2 model
+    try:
+        response = lcpp_llm(
+            prompt=formatted_prompt,
+            max_tokens=256,
+            temperature=0.5,
+            top_p=0.95,
+            repeat_penalty=1.2,
+            top_k=150,
+            echo=True
+        )
+        generated_response = response["choices"][0]["text"]
+        # Remove the input part from the output if it is included
+        if formatted_prompt in generated_response:
+            generated_response = generated_response.replace(formatted_prompt, '').strip()
+        print("Generated response:", generated_response)
+        return generated_response
+    except Exception as e:
+        print("Error in response generation:", str(e))
+        return "Failed to generate response, please check the console for errors."
     
 def classify_sentiment(text):
     # Encode the text using the tokenizer
@@ -214,15 +214,15 @@ iface_ner = gr.Interface(
     title="NER Analysis",
     description="Performs Named Entity Recognition using spaCy and Transformer models."
 )
-# iface_response = gr.Interface(
-#     fn=generate_email_response,
-#     inputs=gr.Textbox(lines=10, placeholder="Enter the email prompt..."),
-#     outputs=gr.Textbox(label="Generated Email Response"),
-#     title="Email Response Generator",
-#     description="Generate email responses using Llama-2 model."
-# )
+iface_response = gr.Interface(
+    fn=generate_email_response,
+    inputs=gr.Textbox(lines=10, placeholder="Enter the email prompt..."),
+    outputs=gr.Textbox(label="Generated Email Response"),
+    title="Email Response Generator",
+    description="Generate email responses using Llama-2 model."
+)
 
 # Using tabs to organize the interfaces
-tabs = gr.TabbedInterface([iface_category, iface_sentiment,iface_summary,iface_ner], ["Category", "Sentiment"," Summary","NER"], css=custom_css)
+tabs = gr.TabbedInterface([iface_category, iface_sentiment,iface_summary,iface_ner,iface_response], ["Category", "Sentiment"," Summary","NER","Response Generator"], css=custom_css)
 tabs.launch(share=True)