Initial commit: skeleton Space for EduShield-AI-Backend

app.py

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+Python 3.12.10 (tags/v3.12.10:0cc8128, Apr  8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)] on win32
+Enter "help" below or click "Help" above for more information.
+import gradio as gr
+from transformers import pipeline
+
+# ---------------------------
+# Load Models
+# ---------------------------
+claim_model_name = "microsoft/deberta-v3-base-zeroshot-v1.1"
+claim_classifier = pipeline("zero-shot-classification", model=claim_model_name, device=0)
+claim_labels = ["factual claim", "opinion", "personal anecdote", "other"]
+
+ai_detect_model_name = "roberta-base-openai-detector"
+ai_detector = pipeline("text-classification", model=ai_detect_model_name, device=0)
+
+nli_model_name = "valhalla/distilbart-mnli-12-3"
+nli_pipeline = pipeline("text-classification", model=nli_model_name, tokenizer=nli_model_name, device=0)
+
+# ---------------------------
+# Functions
+# ---------------------------
+def extract_claims(page_text):
+    sentences = [s.strip() for s in page_text.split(".") if len(s.strip()) > 5]
+    results = []
+    for s in sentences:
+        out = claim_classifier(s, claim_labels)
+        if out["labels"][0] == "factual claim":
+            results.append(s)
+    return results[:5]
+
+def detect_ai(texts):
+    if isinstance(texts, str):
+        texts = [texts]
+    results = []
+    for t in texts:
+        out = ai_detector(t)
+        results.append({"text": t, "label": out[0]["label"], "score": round(out[0]["score"], 3)})
+    return results
+
+def fact_check(claims, evidence_text):
+    if isinstance(claims, str):
+        claims = [claims]
+    results = []
+    for c in claims:
+        out = nli_pipeline(hypothesis=c, sequence_pair=evidence_text)
+        results.append({"claim": c, "label": out[0]["label"], "score": round(out[0]["score"], 3)})
+    return results
+
+# ---------------------------
+# Unified Predict Function
+# ---------------------------
+def predict(page_text="", selected_text="", evidence_text=""):
+    """
+    1. Extract top 5 claims from page_text
+    2. Run AI Detection on claims + selected_text
+    3. Run Fact-Checking on claims + evidence_text if provided
+    """
+    # Extract claims
+    claims = extract_claims(page_text) if page_text else []
+... 
+...     # Combine claims + selected text for AI detection
+...     ai_input = claims.copy()
+...     if selected_text:
+...         ai_input.append(selected_text)
+...     ai_results = detect_ai(ai_input) if ai_input else []
+... 
+...     # Fact-checking: only if evidence is provided
+...     fc_results = fact_check(claims + ([selected_text] if selected_text else []), evidence_text) if evidence_text else []
+... 
+...     return {
+...         "claims": claims,
+...         "ai_detection": ai_results,
+...         "fact_checking": fc_results
+...     }
+... 
+... # ---------------------------
+... # Gradio UI
+... # ---------------------------
+... with gr.Blocks() as demo:
+...     gr.Markdown("## EduShield AI Backend - Predict API & UI")
+... 
+...     page_text_input = gr.Textbox(label="Full Page Text", lines=10, placeholder="Paste page text here...")
+...     selected_text_input = gr.Textbox(label="Selected Text", lines=5, placeholder="Paste selected text here...")
+...     evidence_input = gr.Textbox(label="Evidence Text", lines=5, placeholder="Paste evidence text here...")
+...     predict_btn = gr.Button("Run Predict")
+...     output_json = gr.JSON(label="Predict Results")
+...     predict_btn.click(predict, inputs=[page_text_input, selected_text_input, evidence_input], outputs=output_json)
+... 
+... # ---------------------------
+... # Launch
+... # ---------------------------
+... if __name__ == "__main__":
+...     demo.launch(server_name="0.0.0.0")

requirements.txt

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+gradio==3.42.0
+transformers==4.41.0
+torch>=2.0.0
+sentence-transformers==2.2.2
+requests==2.31.0