Update app.py

app.py

@@ -1,118 +1,25 @@
-import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-from typing import List, Dict
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import os
 
-# Initialize the Hugging Face pipeline (ensure to use a valid model)
-model_name = "your_huggingface_model_name"  # Ensure to use a valid model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Define the model name (replace with your actual model name)
+model_name = "huggingface/transformers"  # Example model name
+
+# Load the tokenizer and model
 try:
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    print("Model and tokenizer loaded successfully!")
 except Exception as e:
-    st.error(f"Error initializing the model '{model_name}': {e}")
-    generator = None  # Set generator to None if model fails to load
-
-# Function to generate OSINT results
-def generate_osint_results(prompt: str, history: List[Dict[str, str]]) -> List[str]:
-    """
-    Simulates generating OSINT-based results from the user's input.
-    Args:
-        prompt (str): The user's input to the simulator.
-        history (List[Dict]): The user's message history with timestamps.
-    Returns:
-        List[str]: A list of OSINT responses from the AI.
-    """
-    # Validate inputs
-    if not prompt.strip():
-        return ["Error: Prompt cannot be empty."]
-    if not isinstance(history, list) or not all(isinstance(h, dict) for h in history):
-        return ["Error: History must be a list of dictionaries."]
-    
-    # Prepare messages for the AI
-    messages = [{"role": "system", "content": f"Responding to OSINT prompt: {prompt}"}]
-    for val in history:
-        if "user" in val:
-            messages.append({"role": "user", "content": val["user"]})
-        if "assistant" in val:
-            messages.append({"role": "assistant", "content": val["assistant"]})
-
-    # Append the current user prompt
-    messages.append({"role": "user", "content": prompt})
-
-    # Generate a response using the Hugging Face model
-    if generator:
-        try:
-            response = generator(messages[-1]["content"], max_length=100, num_return_sequences=1)
-            return [response[0]["generated_text"]]
-        except Exception as e:
-            return [f"Error generating response: {e}"]
-    else:
-        return ["Error: Model initialization failed."]
-
-# Function for fine-tuning the model with the uploaded dataset
-def fine_tune_model(dataset: str) -> str:
-    """
-    Fine-tunes the model using the uploaded dataset.
-    Args:
-        dataset (str): The path to the dataset for fine-tuning.
-    Returns:
-        str: A message indicating whether fine-tuning was successful or failed.
-    """
-    try:
-        # Process the dataset (dummy processing for illustration)
-        with open(dataset, "r") as file:
-            data = file.readlines()
-
-        # Simulate fine-tuning with the provided dataset
-        # Here, you would use the data to fine-tune the model
-        # For this example, we're not actually fine-tuning the model.
-        model.save_pretrained("./fine_tuned_model")
-        return "Model fine-tuned successfully!"
-    except Exception as e:
-        return f"Error fine-tuning the model: {e}"
-
-# Streamlit app interface
-st.title("OSINT Tool")
-st.write("This tool generates OSINT-based results and allows you to fine-tune the model with custom datasets.")
-
-# User input for prompt and message history
-prompt = st.text_area("Enter your OSINT prompt here...", placeholder="Type your prompt here...")
-
-# Initialize session state for message history
-if "history" not in st.session_state:
-    st.session_state.history = []
-
-# Display past conversation
-st.write("### Message History:")
-for msg in st.session_state.history:
-    st.write(f"**User**: {msg['user']}")
-    st.write(f"**Assistant**: {msg['assistant']}")
-
-# Fine-tuning functionality
-dataset_file = st.file_uploader("Upload a dataset for fine-tuning", type=["txt"])
-
-if dataset_file is not None:
-    # Save the uploaded file
-    dataset_path = os.path.join("uploads", dataset_file.name)
-    os.makedirs("uploads", exist_ok=True)
-    with open(dataset_path, "wb") as f:
-        f.write(dataset_file.read())
-    
-    # Fine-tune the model
-    fine_tuning_status = fine_tune_model(dataset_path)
-    st.success(fine_tuning_status)
-
-# Generate OSINT response when prompt is entered
-if st.button("Generate OSINT Results"):
-    if prompt:
-        response = generate_osint_results(prompt, st.session_state.history)
-        st.session_state.history.append({"user": prompt, "assistant": response[0]})
-        st.write("### Generated OSINT Result:")
-        st.write(response[0])
-    else:
-        st.error("Please enter a prompt.")
-
-# Optionally save fine-tuned model
-if os.path.exists("./fine_tuned_model"):
-    st.write("The model has been fine-tuned and saved as `fine_tuned_model`.")
+    print(f"Error loading model: {e}")
+
+# Add your app logic here (e.g., for inference, etc.)
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    return outputs
+
+# Example usage
+if __name__ == "__main__":
+    test_text = "Hello, world!"
+    result = predict(test_text)
+    print(result)