Update README.md

README.md

@@ -32,44 +32,86 @@ import gradio as gr
 from transformers import AutoTokenizer
 from peft import AutoPeftModelForCausalLM
 import torch
+import anthropic
 
-# Assuming the model and tokenizer are correctly set up as per your provided code.
-def summarize_text(text):
-    load_in_4bit = True
-    model = AutoPeftModelForCausalLM.from_pretrained(
+# Assuming the model and tokenizer for Mistral are correctly set up as per your provided code.
+# Let's also assume you have a way to call the Anthropic model, perhaps via an API or another library.
+load_in_4bit = True
+model = AutoPeftModelForCausalLM.from_pretrained(
         "DisgustingOzil/Mistral_summarizer",
         load_in_4bit=load_in_4bit,
         torch_dtype=torch.float16,
     ).to("cuda")
-    tokenizer = AutoTokenizer.from_pretrained("DisgustingOzil/Mistral_summarizer")
-    
+tokenizer = AutoTokenizer.from_pretrained("DisgustingOzil/Mistral_summarizer")
+def summarize_with_mistral(text):
+
+
     summary_prompt = f"""Below is a text that needs to be summarized. Based on the input, write a good summary which summarize all main points.
 
 ### Text:
 {text}
 
 ### Summary:
-""" # The summary part is left empty for generation
+"""  # The summary part is left empty for generation
 
     inputs = tokenizer([summary_prompt], return_tensors="pt").to("cuda")
-    outputs = model.generate(**inputs, max_new_tokens=200, use_cache=True)
+    outputs = model.generate(**inputs, max_new_tokens=150, use_cache=True)
     summary = tokenizer.batch_decode(outputs, skip_special_tokens=True)
     summary_start_index = summary[0].find("### Summary:")
     summary_text = summary[0][summary_start_index:].replace("### Summary:", "").strip()
     return summary_text
-
-# Define the Gradio interface
+summary_1=""
+def summarize_with_anthropic(text):
+      API_KEY="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"
+
+      client = anthropic.Anthropic(
+          # defaults to os.environ.get("ANTHROPIC_API_KEY")
+          api_key=API_KEY,
+      )
+      message = client.messages.create(
+          model="claude-3-haiku-20240307",
+          max_tokens=3214,
+          temperature=0,
+          system="Create Good summary explaining all key points in detail, easy and understandable way",
+          messages=[
+              {
+                  "role": "user",
+                  "content": [
+                      {
+                          "type": "text",
+                          "text": text
+                      }
+                  ]
+              }
+          ]
+      )
+    # Placeholder function to represent summarization with an Anthropic model.
+    # This should be replaced with actual API calls or function calls to the Anthropic model.
+      # summary_1=message.content[0]
+      summary=message.content[0]
+      return summary.text
+
+def summarize_text(text, model_choice):
+    if model_choice == "Mistral 7b":
+        return summarize_with_mistral(text)
+    elif model_choice == "Claude-3-Haiku":
+        return summarize_with_anthropic(text)
+    else:
+        return "Invalid model choice."
+
+# Define the Gradio interface with a dropdown for model selection
 iface = gr.Interface(
     fn=summarize_text,
-    inputs=gr.Textbox(lines=10, label="Input Text"),
+    inputs=[gr.Textbox(lines=10, label="Input Text"), gr.Dropdown(choices=["Mistral 7b", "Claude-3-Haiku"], label="Model Choice")],
     outputs=gr.Textbox(label="Summary"),
     title="Text Summarization",
-    description="Enter text to summarize based on Maxwell's equations and related concepts."
+    description="Enter text to summarize based on Maxwell's equations and related concepts. Select a model for summarization."
 )
 
 # Launch the app
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch(debug=True)
+