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app.py

@@ -1,3 +1,74 @@
-import gradio as gr
-
-gr.load("models/finalgp3/Our_model_GandA").launch(server_port=7860)
+from flask import request,jsonify,Flask
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+app = Flask(__name__)
+
+path_model ="./"
+tokenizer = AutoTokenizer.from_pretrained(path_model)
+model = AutoModelForCausalLM.from_pretrained(path_model, torch_dtype=torch.float32)
+
+def generate_qa_summary(topic, num_questions, temperature=0.4):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    questions_and_answers = []
+    while len(questions_and_answers) < num_questions:
+        prompt = f"{topic} Question:\n"
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+        output = model.generate(
+            max_new_tokens=1000,
+            #length_penalty=1.0,
+            early_stopping=True,
+            input_ids=input_ids,
+            num_return_sequences=1,
+            temperature=temperature,
+            no_repeat_ngram_size=4,
+            pad_token_id=tokenizer.eos_token_id,
+            top_p=0.80,
+            do_sample=True,
+        )
+        response = tokenizer.decode(output[0], skip_special_tokens=True)
+        # Check if the response is valid (contains both 'Question:' and 'Answer:')
+        if "Question:" in response and "Answer:" in response:
+            questions_and_answers.append(response)
+    return questions_and_answers
+
+@app.route("/api/Gen", methods=["POST"])
+def generate_questions1():
+    data = request.get_json()
+    course_name = data["courseName"]
+    num_questions = int(data["numQuestions"])
+    qa_summary = generate_qa_summary(course_name, num_questions)
+    if not qa_summary:
+        return jsonify({"error": "Failed to generate any questions"})
+    first_item = qa_summary[0]
+    topic, _ = (
+        first_item.split("Question:", maxsplit=1)
+        if "Question:" in first_item
+        else (first_item, "")
+    )
+    topic = topic.strip()
+    formatted_summaries = [f"<strong>{topic}:</strong><br><br>"]  # Start with the topic
+    for index, item in enumerate(qa_summary):
+        if "Question:" not in item:
+            item = f"Question: {item}"  # Prepend "Question:" if missing
+        else:
+            parts = item.split("Question:")
+            item = "Question:" + " ".join(
+                parts[1:]
+            )  # Reassemble without extra "Question:"
+
+        _, question_answer = item.split("Question:", maxsplit=1)
+        question, answer = (
+            question_answer.split("Answer:", maxsplit=1)
+            if "Answer:" in question_answer
+            else (question_answer, "No answer provided.")
+        )
+        formatted_question_answer = f"<div class='question-container'><div>-Question: {question.strip()}<br<button onclick='toggleAnswer({index})'><i id='icon{index}' class='fas fa-eye fa'></i></button></div><div id='answer{index}' style='display:none;'>-Answer: {answer.strip()}</div></div><br></div>"
+        formatted_summaries.append(formatted_question_answer)
+    return jsonify(formatted_summaries)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

config.json

@@ -0,0 +1,39 @@
+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 50257
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c888c10456369529f0782ddff007e9730f63ceefffb57c20b08b3898ca9d117a
+size 497774208

tokenizer_config.json

@@ -0,0 +1,22 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}