first move

.gitignore

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+.env
+prompt.txt

app.py

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+import os
+import re
+from dotenv import load_dotenv
+import torch
+from transformers import RobertaForSequenceClassification, RobertaTokenizerFast, pipeline as text_pipeline
+import gradio as gr
+from openai import OpenAI
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get API key from environment
+API_KEY = os.getenv("API_KEY")
+
+# Initialize OpenAI client
+client = OpenAI(
+    base_url="https://integrate.api.nvidia.com/v1",
+    api_key=API_KEY
+)
+
+# Load classification model
+def load_emotion_model(model_path):
+    model = RobertaForSequenceClassification.from_pretrained(model_path)
+    tokenizer = RobertaTokenizerFast.from_pretrained(model_path)
+    return model, tokenizer
+
+# Map prediction to readable labels
+def map_to_labels(label):
+    return "Happy/Positive Mindset" if label.lower() == "positive" else "Depressed/Negative Mindset"
+
+# Classify mental state based on user input
+def classify_emotion(user_input, model, tokenizer, device):
+    nlp = text_pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)
+    result = nlp(user_input)
+    return map_to_labels(result[0]['label'])
+
+# Analyze emotion using the LLM
+def emotion_analysis(user_input):
+
+    # # Validate input
+    # if not user_input.strip():  # Check for empty or blank input
+    #     progress_callback("Please provide valid input before submitting.", False)
+    #     return "No input provided.", ""
+
+    # Load model
+    model_path = "mentalhealth-roberta-base_nemotron_model"  # Replace with your model path
+    model, tokenizer = load_emotion_model(model_path)
+    device = 0 if torch.cuda.is_available() else -1
+
+    # Step 1: Classify emotion
+    predicted_emotion = classify_emotion(user_input, model, tokenizer, device)
+
+    # Step 2: Generate LLM response
+    prompt = f"""
+    Task: You are a social psychologist specializing in Roy Baumeister's six-stage theory of emotional progression. Your task is to analyze emotional states based on user input while adhering to specific response boundaries.
+
+    [Input Information]:
+    **User Input**: "{user_input}"
+    **Model Output**: "{predicted_emotion}"
+
+    Specifics:
+    1. Strictly respond only to questions or input related to mental health or emotional well-being. For unrelated input, reply with: "Not a valid question." 
+       - Example: If the user asks about weather, sports, or other unrelated topics, respond with: "Not a valid question."
+    2. Use the **User Input** as the primary source for determining the emotional state, while considering the **Model Output** ("happy" or "depressed") only as a secondary reference.
+    3. Assign the user’s emotional state to one of Roy Baumeister’s six stages of emotional progression:
+       - Stage 1: Falling short of expectations
+       - Stage 2: Attributions to self
+       - Stage 3: High self-awareness
+       - Stage 4: Negative affect
+       - Stage 5: Cognitive deconstruction
+       - Stage 6: Disinhibition
+    4. Provide specific recommendations for the assigned stage:
+       - If the user is **depressed**, suggest stage-specific remedies to improve their emotional state.
+       - If the user is **happy**, suggest strategies to maintain or enhance their happiness.
+    5. Prioritize clarity, empathy, and practicality in your analysis and suggestions.
+
+    [Response Rules]:
+    - Do NOT attempt to provide an output if the input is not related to mental health.
+    - Always analyze the user’s input independently, even if it conflicts with the model’s predicted output.
+
+    [Desired Output Format]:
+    Emotional Analysis:
+    I'd say you're feeling: <Happy/Depressed>
+    Emotional Stage: <Stage and brief reasoning>
+    Suggested Remedies/Strategies: <Practical advice based on the stage>
+    """
+
+    try:
+        completion = client.chat.completions.create(
+            model="nvidia/nemotron-4-340b-instruct",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.2,
+            top_p=0.7,
+            max_tokens=512,
+            stream=True
+        )
+
+        # Iterate over the streaming response
+        response = ""
+        for chunk in completion:
+            if chunk.choices[0].delta.content is not None:
+                print(chunk.choices[0].delta.content, end="")
+                # response = chunk.choices[0].delta.content
+                response_chunk = chunk.choices[0].delta.content
+                response += response_chunk
+            else:
+                print(f"Unexpected chunk format: {chunk}")
+
+    except Exception as e:
+        response = f"An error occurred while processing the response: {e}"
+    response= str(response).replace("*", '')
+    return response
+
+def extract_analysis_details(analysis_text):
+    feelings_match = re.search(r"I'd say you're feeling:\s*([^\n]+)", analysis_text)
+    feelings = feelings_match.group(1).strip() if feelings_match else "Not Found"
+    if feelings.lower() == "happy":
+        feelings = feelings + " with Positive Mindset"
+    elif feelings.lower() == "depressed":
+        feelings = feelings + " with Negative Mindset"
+    else:
+        feelings
+
+    # Extract emotional stage
+    stage_match = re.search(r"Emotional Stage:\s*([^\n.]+)", analysis_text)
+    emotional_stage = stage_match.group(1).strip() if stage_match else "Not Found"
+
+    # Regex to match the section header and capture from there to the end
+    pattern = r"(Suggested Remedies|Suggested Remedies/Strategies|Suggested Strategies):.*"
+    match = re.search(pattern, analysis_text, re.DOTALL)
+    suggestions = match.group(0).strip() if match else "No matching section found."
+    # print(suggestions)
+
+    if feelings == "Not Found":
+        feelings = "Not a valid question."
+    return feelings, emotional_stage, suggestions
+
+# Gradio interface with input validation
+def validate_and_run(user_input):
+    if not user_input.strip():  # Check if the input is empty or just spaces
+        return "Please provide valid input before submitting.", "Not Applicable", "Not Applicable"
+    else:
+        response = emotion_analysis(user_input)
+        return extract_analysis_details(response)
+
+
+# Gradio interface
+iface = gr.Interface(
+    fn=validate_and_run,
+    inputs=gr.Textbox(#lines=2,
+        label="How are you feeling today?",
+        placeholder="Share your thoughts here...!"),
+    outputs=[
+        # gr.Textbox(label="Analysing Your State of Mind...."),
+        # gr.Textbox(label="Providing Best Strategies")
+        # gr.Textbox(label="Original"),
+        gr.Textbox(label="Feelings"),
+        gr.Textbox(label="Emotional Stage"),
+        gr.Textbox(label="Providing Best Strategies")
+    ],
+    # live=True,
+    title="Analyze your emotions and generate stage-specific psychological insights\n",
+    # title = "Emotion Analysis and Dynamic Response Generator"
+    # description="Analyze your emotions and receive dynamic psychological insights."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+

mentalhealth-roberta-base_nemotron_model/config.json

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+{
+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "negative",
+    "1": "positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "negative": 0,
+    "positive": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

mentalhealth-roberta-base_nemotron_model/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20f6612a4917ba6e0b7d9b062ec628cd77b29b61a9053e6ba875041adccfcb82
+size 498612824

mentalhealth-roberta-base_nemotron_model/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

mentalhealth-roberta-base_nemotron_model/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "unk_token": "<unk>"
+}

requirements.txt

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+python-dotenv
+gradio
+transformers
+torch
+openai
+
+