Added gradio to app.py

app.py

@@ -1,29 +1,23 @@
 import os
-
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
 from typing import List
 import torch
 from sentence_transformers import SentenceTransformer, util
 from textblob import TextBlob
+import gradio as gr
 
 model = SentenceTransformer("paraphrase-MiniLM-L12-v2")
 model.eval()
 
 
 def calculate_semantic_similarity(
-    claim: str, sentences: List[str], similarity_threshold: float = 0.4
+    claim: str, sentences_input: str, similarity_threshold: float = 0.4
 ) -> float:
     """
-    Calculates a weighted score representing how well a list of sentences supports a claim.
-    Args:
-        claim (str): The claim to be verified.
-        sentences (List[str]): A list of sentences to check against the claim.
-        similarity_threshold (float, optional): The minimum similarity score for a
-                                               sentence to be considered "supporting". Defaults to 0.5.
-
-    Returns:
-        float: A weighted score between 0.0 and 1.0.
+    Accepts a claim and newline-separated sentences. Returns a weighted similarity score.
     """
+    sentences = [s.strip() for s in sentences_input.split("\n") if s.strip()]
     if not sentences:
         return 0.0
 
@@ -44,7 +38,6 @@ def calculate_semantic_similarity(
             elif claim_sentiment * sentence_sentiment < 0:
                 similarity *= 0.9
 
-            print(f"Sentence: {sentence}\nSimilarity: {similarity:.2f}\n")
             similarity = max(0.0, min(1.0, similarity))
             all_scores.append(similarity)
 
@@ -54,35 +47,21 @@ def calculate_semantic_similarity(
     if proportion_supporting >= 0.30:
         final_score = sum(supporting_scores) / len(supporting_scores)
     else:
-        average_all_scores = sum(all_scores) / len(all_scores)
-        # penalty = 0.80  # 20% reduction
-        final_score = average_all_scores  # * penalty
+        final_score = sum(all_scores) / len(all_scores)
 
-    return final_score
+    return round(final_score, 4)
 
 
-if __name__ == "__main__":
-    claim_to_verify = "The new government policy is expected to boost the economy."
-    evidence_sentences = [
-        "The recent legislation is projected to stimulate significant economic growth.",  # High similarity
-        "Market analysts are optimistic about the financial future following the announcement.",  # High similarity
-        "However, some critics argue that the policy might lead to unforeseen inflation.",  # Low similarity
-        "The stock market reacted positively, showing a slight increase.",  # Medium similarity
-        "This is considered a poor decision for the nation's financial stability by some experts.",  # Opposing sentiment
-        "The primary goal of the initiative is to create jobs and encourage consumer spending.",  # High similarity
-        "Unemployment rates are expected to decline in the coming months.",  # High similarity
-        "There has been some public disapproval regarding the policy's rollout.",  # Low similarity
-        "This will surely lead to a stronger and more resilient economy.",  # High similarity
-        "Financial experts have voiced concerns about the potential long-term consequences.",  # Opposing sentiment
-    ]
+iface = gr.Interface(
+    fn=calculate_semantic_similarity,
+    inputs=[
+        gr.Textbox(label="Claim"),
+        gr.Textbox(lines=10, label="Evidence Sentences (one per line)"),
+        gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.4, label="Similarity Threshold")
+    ],
+    outputs=gr.Number(label="Final Weighted Support Score"),
+    title="Claim Support Checker",
+    description="Input a claim and evidence sentences to calculate how strongly the evidence supports the claim."
+)
 
-    final_score = calculate_semantic_similarity(claim_to_verify, evidence_sentences)
-
-    print(f"The final weighted support score for the claim is: {final_score:.2f}")
-
-    if final_score > 0.65:
-        print("Interpretation: The claim is strongly supported by the evidence. ✅")
-    elif final_score > 0.4:
-        print("Interpretation: The claim has moderate support from the evidence. 🤔")
-    else:
-        print("Interpretation: The claim has weak support from the evidence. ❌")
+iface.launch()