Create app.py

app.py

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+import gradio as gr
+import requests
+import subprocess
+import time
+import json
+
+from ollama import chat
+from ollama import ChatResponse
+
+
+def start_ollama_server():
+    # Start Ollama server in the background
+    print("Starting Ollama server...")
+    subprocess.Popen(["ollama", "serve"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    time.sleep(5)  # Give some time for the server to start
+
+    # Pull the required model
+    print("Pulling the required model...")
+    subprocess.run(["ollama", "pull", "llama3.2:1b"], check=True)
+
+    print("Pulling the required model...")
+    subprocess.Popen(["ollama", "run", "llama3.2:1b"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    print("Ollama started model.")
+
+# Function to send a prompt to the Ollama server and return the response
+def ask_ollama(question):
+    # url = "http://localhost:11434/api/generate"
+    # headers = {"Content-Type": "application/json"}
+    # data = {
+    #     "model": "llama3.1",
+    #     "prompt": prompt,
+    #     "format": "json",
+    #     "stream": False
+    # }
+    # try:
+    #     # Send the POST request to the API
+    #     response = requests.post(url, headers=headers, json=data)
+    #     response.raise_for_status()  # Raise an exception for HTTP errors
+    #     result = response.json()  # Parse the JSON response
+    #
+    #     # Extract and clean the "response" field
+    #     actual_response = result.get("response", "").strip()
+    #     print(actual_response)
+    #     return actual_response if actual_response else "No response found"
+    # except requests.exceptions.RequestException as e:
+    #     return f"Error: {str(e)}"
+
+    prompt_template = f"""
+    ### You are an expert in the subreddit r/AmItheAsshole. 
+
+    ### The task for you is to classify the given text content as YTA or NTA label and give an explanation for the same. 
+
+    ### The output format is as follows:
+    "YTA" or "NTA", explanation for the label.
+
+    ### Input Text :  {question}
+    """
+
+    response: ChatResponse = chat(model='llama3.2:1b', messages=[
+        {
+            'role': 'user',
+            'content': prompt_template,
+        },
+    ])
+    print(response['message']['content'])
+    # or access fields directly from the response object
+    return response['message']['content']
+
+
+# Gradio Interface
+def gradio_interface(prompt):
+    return ask_ollama(prompt)
+
+
+# Build the Gradio app
+with gr.Blocks() as demo:
+    gr.Markdown("# Ollama Server Interface")
+    gr.Markdown("Ask questions and get responses from the Ollama server.")
+
+    with gr.Row():
+        input_prompt = gr.Textbox(label="Enter your question", placeholder="Type your question here...")
+
+    with gr.Row():
+        submit_button = gr.Button("Ask")
+
+    with gr.Row():
+        output_response = gr.Textbox(label="Response", lines=10)
+
+    submit_button.click(gradio_interface, inputs=input_prompt, outputs=output_response)
+
+# Launch the app
+if __name__ == "__main__":
+    start_ollama_server()
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+